Best Artificial Intelligence Programs

Ranked in 2023, part of Best Science Schools

Artificial intelligence is an evolving field that

Artificial intelligence is an evolving field that requires broad training, so courses typically involve principles of computer science, cognitive psychology and engineering. These are the best artificial intelligence programs. Read the methodology »

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Thor Olavsrud

Top 10 AI graduate degree programs

Thinking about getting your graduate degree in artificial intelligence? Here are 10 of the top schools with AI degrees worth pursuing.

He Works on Desktop Computer in College. Applying His Knowledge in Writing Code, Developing Software.

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI.

U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2022 and early 2023 in chemistry, computer science, earth science, mathematics, and physics.

Here are the top 10 programs that made the list that have the best AI graduate programs in the US.

1. Carnegie Mellon University

The Machine Learning Department of the School of Computer Science at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning. CALD drew from the Statistics Department and departments within the School of Computer Science, as well as faculty from philosophy, engineering, the business school, and biological science.

Carnegie Mellon says the department’s research strategy is to maintain a balance between research into the cure statistical-computational theory of machine learning, and research inventing new algorithms and new problem formulations relevant to practical applications.

The Machine Learning Department offers both doctoral and master’s programs in machine learning, including:

  • PhD in Machine Learning (ML)
  • Joint PhD Program in Statistics & Machine Learning (offered jointly with the Statistics Department)
  • Joint PhD Program in Machine Learning & Public Policy (offered jointly with the Heinz College Schools of Public Policy, Information Systems, and Management)
  • Joint PhD Program in Neural Computation & Machine Learning (offered jointly with the Neuroscience Institute)
  • Primary Master’s in Machine Learning
  • 5th-Year Master’s in Machine Learning (a one-year program for current CMU students)
  • Secondary Master’s in Machine Learning (for current CMU PhD students, faculty, or staff)

2. Massachusetts Institute of Technology (MIT)

The MIT Department of Electrical Engineering and Computer Science (EECS) is the largest academic department at MIT. A joint venture with the MIT Schwarzman College of Computing offers three overlapping sub-units in electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

MIT says AI+D’s research explores the foundations of machine learning and decision systems (AI, reinforcement learning, statistics, causal inference, systems, and control), the building blocks of embodied intelligence ( computer vision , NLP , robotics), applications to real-world autonomous systems, life sciences, and the interface between data-driven decision-making and society.

The EECS Department graduate degree programs include:

  • Master of Science (MS), which is required of students pursuing a doctoral degree
  • Master of Engineering (MEng), for MIT EECS undergraduates
  • Electrical Engineer (EE)/Engineer in Computer Science (ECS)
  • Doctor of Philosophy (PhD)/Doctor of Science (ScD), awarded interchangeably

3. Stanford University

Stanford University’s Computer Science Department is part of the School of Engineering . The Stanford AI Lab (SAIL) was founded in 1962 as a center of excellence for AI research, teaching, theory, and practice. In addition to its in-person programs, Stanford Online offers the Artificial Intelligence Graduate Certificate entirely online. The AI program focuses on the principles and technologies that underlie AI, including logic, knowledge representation, probabilistic models, and machine learning.

Stanford offers both PhDs and an MSCS with an AI specialization.

4. University of California – Berkeley

The University of California – Berkeley Department of Electrical Engineering and Computer Sciences focuses its foundational research in core areas of deep learning, knowledge representation, reasoning, learning, planning, decision-making, vision, robotics, speech, and NLP. There are also efforts to apply algorithmic advances to applied problems in a range of areas, including bioinformatics, networking and systems, search, and information retrieval. It’s closely associated with the Berkeley Artificial Intelligence Research (BAIR) Lab.

Berkeley offers both PhDs and master’s programs.

5. University of Illinois – Urbana-Champaign

The University of Illinois – Urbana-Champaign Grainger College of Engineering focuses its AI and machine learning program on computer vision, machine listening, NLP, and machine learning. In computer vision, the AI group faculty are developing novel approaches for 2D and 3D scene understanding from still images and video, low-shot learning, and more. The machine listening faculty is working on sound and speech understanding, source separation, and applications in music and computing. The machine learning faculty studies the theoretical foundations of deep and reinforcement learning; develops novel models and algorithms for deep neural networks, federated, and distributed learning; and addresses issues related to scalability, security, privacy, and fairness of learning systems.

The university offers a CS PhD program, CS MS program, a professional master’s of computer science program, and a fifth-year master’s program.

6. Georgia Institute of Technology

Georgia Tech College of Computing says AI and machine learning represent a large swath of its faculty and research interests, including constructing top-to-bottom and bottom-to-top models of human-level intelligence; building systems that can provide intelligent tutoring; creating adaptive and intelligent entertainment systems; making systems that understand their own behavior; and constructing autonomous agents that can adapt in dynamic environments.

Different groups within the school emphasize different areas of research. The core faculty comes from the School of Interactive Computing, but there are also machine learning faculty in the schools of Computer Science and Computational Science & Engineering.

Georgia Tech offers both master’s and doctoral programs, including a PhD in Machine Learning.

7. University of Washington

The University of Washington Paul G. Allen School of Computer Science & Engineering offers an AI group that studies the computational mechanisms underlying intelligent behavior. Research areas include machine learning, NLP, probabilistic reasoning, automated planning, machine reading, and intelligent user interfaces. It collaborates closely with the Allen Institute for Artificial Intelligence (AI2).

The University of Washington offers a combined bachelor’s of science (BS)/master’s of science (MS) program created with industry-bound students in mind, a full-time PhD program, a professional master’s program (a part-time, evening program), and a postdoctoral research program.

8. University of Texas – Austin

The University of Texas at Austin Department of Computer Science is focused on computer vision, evolutionary computation, machine learning, multimodality, NLP, neural networks, reinforcement learning, and robotics. It hosts myriad research centers and labs, including the Laboratory for Artificial Intelligence, which opened in 1983 and investigates the central challenges of machine cognition, including machine learning, knowledge representation, and reasoning. Some others include the Institute for Foundations of Machine Learning, Machine Learning Lab, Machine Learning Research Group, and Neural Networks Research Group.

The University of Texas offers a PhD program, master’s program, online master’s program in computer science, online master’s program in data science, and five-year BS/MS programs.

9. Cornell University

Cornell Bowers CIS College of Computing and Information Science has been building out its AI group since the 1990s. In 2021, it launched a new initiative, a new Radical Collaboration , laid out by scholars across the university to advance its reputation as a leader in AI research, education, and ethics. The initiative expands faculty working in core areas and other domains affected by AI advances. Recent interdisciplinary collaborations across the Ithaca Campus, Cornell Tech, and Weill Cornell Medicine have applied AI to issues ranging from sustainable agriculture and urban design to cancer detection, improving autonomous vehicles, and parsing quantum matter.

Cornell offers a Master of Engineering in Computer Science program, as well as a Computer Science Master’s of Science program, and PhD program.

10. University of Michigan – Ann Arbor

The University of Michigan Computer Science and Engineering division offers an AI program comprised of multidisciplinary researchers studying rational decision making, distributed systems of multiple agents, machine learning, reinforcement learning, cognitive modeling, game theory, NLP, machine perception, healthcare computing, and robotics.

The university says research in the AI laboratory tends to be highly interdisciplinary, building on ideas from computer science, linguistics, psychology, economics, biology, controls, statistics, and philosophy.

The University of Michigan offers a PhD in CSE, master’s in CSE, and master’s in data science.

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Thor Olavsrud

Thor Olavsrud covers data analytics, business intelligence, and data science for CIO.com. He resides in New York.

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Best Online Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salary

Machine learning is a rapidly growing, fascinating field dealing with algorithm development that can be used to make predictions from data. The best online PhD in Machine Learning prepares students for a career in this promising field.

The best online doctorates in machine learning offer students a comprehensive education in all aspects of the field. Students are also provided with the opportunity to choose a specialization such as deep learning, natural language processing , or computer vision. Find out in this article what machine learning PhD online degree program best fits you and the machine learning jobs for graduates.

Find your bootcamp match

Can you get a phd in machine learning online.

Yes, you can get a PhD in Machine Learning online. The online learning system has seen rapid growth in many academic fields and has given students the opportunity to virtually access the academic curriculum remotely.

Many online PhD programs in the United States are accredited and designed with working professionals in mind. Online learning is a great way to earn a doctorate without sacrificing your day job, and in most cases, online students can complete their entire academic journey without stepping foot on campus.

Is an Online PhD Respected?

Yes, an online PhD is respected when it is obtained from an accredited institution in the US. A PhD from an unaccredited school is regarded as just an expensive piece of paper by many other academic institutions.

In regard to employment, many companies and organizations respect an online PhD, holding it to the same standard as an in-person PhD. However, some employers prefer in-person degrees and will disregard online degrees. Ensure your potential future employer accepts online degrees as credible education.

What Is the Best Online PhD Program in Machine Learning?

The best online PhD program in machine learning is at Clarkson University in Potsdam, New York. It is regionally accredited by the Middle States Commission on Higher Education and has an excellent reputation within the academic community, a student-to-faculty ratio of 12 to one, and one in five of its 44,000 alumni is a CEO or executive.

Why Clarkson University Has the Best Online PhD Program in Machine Learning

Clarkson University has the best machine learning PhD program not only because it is accredited by the Middle States Commission on Higher Education (MSCHE) but also because of its US News & World Report ranking. MSCHE is a regionally recognized accreditation association that uses a rigorous and comprehensive system for the purpose of accreditation.

Referring to US News & World Report, Clarkson University is ranked 127 for best national universities out of 4000 degree-granting academic institutions in the United States and 49 for best value schools.

Best Online Master’s Degrees

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Online PhD in Machine Learning Admission Requirements

The admission requirements for an online PhD in Machine Learning typically include a master’s degree or Bachelor’s in Machine Learning or a related subject like the field of engineering. Moreover, prepare to submit official transcripts from previously attended postsecondary institutions and GRE test scores.

Additionally, you may be asked to submit three letters of recommendation, a statement of purpose, a CV or resume, and prove your knowledge of calculus and your fluency in computer programming languages like Python and Java. Below is a list of the typical admission requirements needed by distinct schools that offer a machine learning PhD program.

  • Master’s or bachelor’s degree in a relevant field
  • Official transcripts and GRE test scores
  • Letters of recommendation
  • Statement of purpose
  • CV or resume
  • Knowledge of programming and calculus

Best Online PhDs in Machine Learning: Top Degree Program Details

Best online phds in machine learning: top university programs to get a phd in machine learning online.

The top university programs to get a PhD in Machine Learning are at Clarkson University, Aspen University, Capitol Technology University, The University of Rhode Island, and The University of the Cumberlands, among other distinct schools.

This section discusses the properties, requirements, and descriptions of the best universities offering online PhD in Machine Learning programs. We have created this list below to narrow down your school search for these graduate-level in-depth study programs.

Aspen University is a Distance Education Accrediting Commission accredited university. It was established in 1987 as a private for-profit online university offering undergraduate and graduate degrees in computer science, business information systems, and project management.

Aspen University in Phoenix, Arizona is a known member of the Council for Adult and Experiential Learning and is dedicated to supporting adult learners in achieving a professional career in whatever field they desire.

DSc in Computer Science

This doctoral degree teaches students the theory and practical application of computer science in data science, application design, and computer architecture. It contains 20 courses, including artificial intelligence, risk analysis, and system metrics. 

These courses are offered online and aim to impart students with the necessary skills for improving existing technology, as well as evaluating and applying them. It also contains courses that aid doctoral students in carrying out their research dissertations.

DSc in Computer Science Overview

  • Accreditation: Distance Education Accrediting Commission
  • Program Length: 5 years and 7 months
  • Acceptance Rate: N/A
  • Tuition and Fees: $375/month

DSc in Computer Science Admission Requirements

  • Master’s degree
  • Statement of goals
  • Minimum of 3.0 GPA
  • Must know about object-oriented development

Capitol Technology University was founded in 1927 and offers online programs at the undergraduate, graduate, and doctoral levels. The areas of study in which these online programs are offered include business, technology, and the field of engineering.

PhD in Artificial Intelligence

This is a research-based PhD program that offers students the opportunity to conduct research in any field of their choice. Throughout the program, student work must be approved by the academic supervisor. Students are to submit a thesis and give an oral presentation which will be supervised by an expert in the field.

PhD in Artificial Intelligence Overview

  • Accreditation: Middle States Commission on Higher Education
  • Program Length: 2 to 3 years
  • Tuition and Fees: $933/credit

PhD in Artificial Intelligence Admission Requirements

  • Application fee of $100
  • Master’s degree in a relevant field
  • Minimum of five years of related work experience
  • Two recommendation letters

Founded in 1973, City University of Seattle is recognized as a top 10 educator of adults nationwide, as ranked by the US News & World Report for school rankings. It offers online undergraduate, graduate, and doctoral programs designed for working professionals

PhD in Information Technology

The program’s curriculum consists of courses in machine and deep learning. Candidates are given the option to propose their depth of study, which requires approval from the academic director. The program consists of core courses, concentration courses, a comprehensive examination, a research core, and a dissertation. 

PhD in Information Technology Overview

  • Accreditation: Northwest Commission on Colleges and Universities
  • Program Length: Flexible
  • Acceptance Rate: 100% due to open admission policy
  • Tuition and Fees: $765/credit

PhD in Information Technology Admission Requirements

  • A master’s degree from an accredited or recognized institution
  • CV and resume, and three references letters 
  • Proof of English proficiency
  • Interview with admissions advisor
  • State goals related to your academic work

Founded in 1896 to honor Thomas S. Clarkson, Clarkson University offers flexible online degree programs at the undergraduate and graduate levels. It is a research university that leads in technology education. 

PhD in Computer Science

This doctoral program places emphasis on areas such as artificial intelligence , software, security, and networking. Current students are required to complete 36 credits of computer science foundation and research-oriented courses, elective courses, achieve candidacy within the first two years of the program, and propose and defend a thesis.

PhD in Computer Science Overview

  • Program Length: 3 years
  • Tuition and Fees: $1,533/credit

PhD in Computer Science Admission Requirements

  • Complete the online application form
  • Resume, statement of purpose, and three letters of recommendation
  • English proficiency test for international applicants (TOEFL, IELTS, PTE, and Duolingo English Test)

Northcentral University is a private university established in 1996 and is designed for flexibility by offering programs of distance learning for working professionals. It practices a distinctive one-to-one learning system and has a dedicated doctoral faculty.

In this doctorate program, besides writing papers about past research, students are allowed to propose their research. Its curriculum consists of subjects such as software engineering , artificial intelligence, data mining, and cyber security. Through the course, students conduct research and examine real-world issues in the field of computer science.

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  • Accreditation: WASC Senior College and University Commission
  • Program Length: 3 years and 4 months
  • Tuition and Fees: $1,094/credit
  • Master’s degree from an accredited institution
  • Official transcripts
  • English proficiency exam score for international students

Nova Southeastern University was founded in 1964 in Fort Lauderdale, Florida. It offers online graduate and undergraduate courses and conducts a wide variety of interdisciplinary healthcare research. It is home to national athletics champions and Olympians.

This program provides research in computer science. Its format of learning combines both traditional and online instruction designed with consideration for working professionals . Its coursework consists of research in computer science areas, including cyber security, software engineering, and artificial intelligence.

  • Accreditation: Southern Association of Colleges and Schools, Commission on Colleges
  • Program Length: Not specified
  • Tuition and Fees: $1,282/credit
  • Online application and $50 application fee
  • A bachelor’s or master’s degree in a relevant field from a regionally accredited institution
  • GPA of at least 3.20 
  • Official transcripts from all institutions attended 
  • A resume  
  • Essay, and three letters of recommendation

The University of North Dakota was founded in 1883, six years before North Dakota was made a state. Today, it offers several academic programs in undergraduate, graduate, and doctoral fields and is known for conducting research in areas that include medicine, aerospace, and engineering.

This PhD in Computer Science curriculum consists of courses in machine learning, software engineering, applications of AI, computer forensics, and computer networks which benefit students by granting them proficiencies in areas such as artificial intelligence, compiler design, operating systems, simulation, databases, and networks.

  • Accreditation: Higher Learning Commission
  • Program Length: 4 to 5 years
  • Tuition and Fees: $545.16/credit (in state); $817.73/ credit (out of state)
  • Application fee of $35
  • Master’s or bachelor’s degree in engineering or a related science field
  • GPA of 3.0 on a 4.0 scale and GRE test score
  • Official copy of all college and university academic transcripts
  • Statement of academic goals and three letters of recommendation
  • Expertise in a high-level programming language and basic knowledge of data structures, formal languages, computer architecture and OS, calculus, statistics, and linear algebra 
  • English language proficiency

The University of Rhode Island is a public research institution founded in 1892. It conducts extensive research in the field of science. It offers online, on-site, and hybrid programs at the graduate and undergraduate levels, as well as certificate programs.

In this PhD in Computer Science program, students are involved in research geared toward producing new intellectual and innovative contributions to the field of computer science. It offers a combination of on-campus, online, and day and evening classes. It consists of courses in machine learning, artificial intelligence, software engineering, and systems simulation.

  • Accreditation: New England Commission of Higher Education
  • Program Length: 4 years
  • Tuition and Fees: $14,454/year (in-state); $27,906/ year (out of state)
  • An undergraduate degree from a regionally accredited institution in the US
  • A minimum GPA of 3.0
  • All official college transcripts
  • Personal statement
  • An application fee of $65

Founded in 1888 by Baptist ministers in Williamsburg KY, today the University of the Cumberlands offers online master's and doctoral degree programs in the fields of education, information technology, and business.

The program requires 18 credit hours of core courses which include information technology geared toward creating machine learning engineers . Its curriculum focuses on predictive analytics and other skills students need to become experts in cyber crime security, big data, and smart technologies.

Students have the option to specialize in information systems security, information technology, digital forensics, or blockchain technologies. Students will complete 21 credit hours of professional research while working toward a dissertation.

  • Tuition and Fees: $500/credit
  • A master’s degree from a regionally accredited institution
  • TOEFL for non-native English speakers
  • Application fee of $30

Wright State University was first seen in 1964 as a branch campus for Ohio State University and Miami University. It is a Carnegie classified research university and offers research at the undergraduate, graduate, and doctoral levels.

PhD in Computer Science and Engineering

This degree is awarded to students who show excellence in study and research that significantly contributes to the field of computer science and engineering. The degree requirements include an A grade completion of the core coursework in two areas and at least a B in the third. 

Students are to complete a minimum of 18 hours of residency research before taking the candidacy exam, which must be completed with a satisfactory grade. Also, a minimum of 12 hours of dissertation research is needed before the dissertation defense, which has to be approved.

PhD in Computer Science and Engineering Overview

  • Program Length: 10 years time limit
  • Tuition and Fees: $660/credit (in state); $1,125/ credit (out of state)
  • Bachelor’s or master’s degree in a related discipline (computer science or engineering)
  • Minimum GPA of 3.0 if admitted with a bachelor’s degree or 3.3 with a master’s degree
  • GRE general test portion
  • TOEFL score for non-native English speakers
  • Knowledge of high-level programming languages, computer organization, operating systems, data structures, and computer systems design
  • A record that indicates potential for a career in research

Online Machine Learning PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Machine Learning?

It is very hard to complete an online PhD in Machine Learning. According to a paper published in the International Journal of Doctoral Studies, there is a PhD attrition rate of 50 percent in the US within the past 50 years. Therefore, the graduation rate for doctorate students is approximately 50 percent.

How Long Does It Take to Get a PhD in Machine Learning Online?

It takes about four years to get a PhD in Machine Learning online, which is fast when compared to a traditional in-person PhD program which may take over seven years to complete. Online PhD programs are accelerated by default, so the curriculum focuses on the major needs of a PhD graduate in the areas of research, thesis, and dissertation.

Students may be able to reduce the time spent pursuing a PhD in Machine Learning by first acquiring a master’s degree in the field. If you choose to pursue a PhD on a part-time schedule as opposed to full-time study, it will significantly increase the time it takes to acquire the degree.

How Hard Is an Online Doctorate in Machine Learning?

Getting an online doctorate in machine learning is very hard, as are most graduate programs. Besides the rigorous research, strict requirements, deadlines, qualification examinations, and dissertations, other challenges may exist, such as limited student connection with the faculty members, isolation, financial issues, and lack of an adequate work-life balance .

Getting a doctorate in any field is not easy. In fact, there is research to suggest that online doctorate students face challenges regarding culture and academia. As a result of these challenges, many students drop out from their PhD programs.

Best PhD Programs

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What Courses Are in an Online Machine Learning PhD Program?

The courses in an online machine learning PhD program include an introduction to machine learning and deep learning, artificial intelligence, statistical theories, data mining , system simulation, computer programming, and software development.

Main Areas of Study in a Machine Learning PhD Program

  • Machine learning
  • Deep learning
  • Artificial intelligence
  • Databases and data mining
  • Statistical theory
  • Software engineering
  • Systems simulation

How Much Does Getting an Online Machine Learning PhD Cost?

On average, it costs $19,314 per year to get a PhD in Machine Learning, according to the National Center of Education Statistics (NCES). However, this figure is not fixed, as the total tuition for a PhD program varies from school to school.

Private institutions generally cost more than public institutions, but there are funding opportunities for PhD students. Some PhD programs may guarantee financial aid for all their students regardless of merit.

How to Pay for an Online PhD Program in Machine Learning

You can pay for an online PhD in Machine Learning by taking advantage of student loans, scholarships, grants, teaching and research assistantships, graduate assistantships, and fellowship assistantships. As a result, most PhD students spend less than the tuition fee displayed on a school’s website.

How to Get an Online PhD for Free

You cannot get an online PhD in Machine Learning for free. However, there are ways to reduce the cost, or get partial tuition discounts and stipends through graduate assistantships, fellowships, scholarships, or grants.

What Is the Most Affordable Online PhD in Machine Learning Degree Program?

The most affordable online PhD in Machine Learning based on cost per credit is at Aspen University in Phoenix, Arizona. It charges $375 per month, which, when multiplied by the 67 months it takes to complete the program, results in a total of $25,125 for the entire program. This is more affordable compared to a school like Clarkson University, which charges $1,533 per credit hour.

Most Affordable Online PhD Programs in Machine Learning: In Brief

Why you should get an online phd in machine learning.

You should get an online PhD in Machine Learning because having a PhD offers you a stronger advantage in terms of employability, salary, and in your career in general that would otherwise be unavailable with just a bachelor’s and master’s degree.

Top Reasons for Getting a PhD in Machine Learning

  • Research opportunities. PhD students get the opportunity to be involved in rigorous and innovative research that may positively impact humanity, add to the world’s knowledge, and improve the lives of others.
  • Expertise development. A PhD is the highest level of academic degree, and as a result, PhD holders have expert-level knowledge in whichever field they acquire a PhD in. However, it is advised to only get a PhD if you are very interested in the field and willing to explore your interest and expand your understanding through cutting-edge research.
  • Access to better jobs. There are lots of bachelor’s and master’s degree graduates in the job market, and earning a PhD will help you stick out from the crowd. A PhD reveals career opportunities that may not be available to bachelor’s and master’s degree grads.
  • Networking opportunities . During a PhD program, students are in contact with top lecturers and academic experts by attending guest lectures, conferences, seminars, and workshops. Students can network with colleagues and classmates, which helps put them in a good position after their academic journey.

Best Master’s Degree Programs

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What Is the Difference Between an On-Campus Machine Learning PhD and an Online PhD in Machine Learning?

The difference between an on-campus machine learning PhD and an online PhD in Machine Learning is primarily the mode of learning. Online PhDs are as rigorous and effective as their on-campus counterparts.

However, there may be some slight differences between the two in terms of cost, schedule, quality, and funding. Some of the differences that may exist are discussed below.

Online PhD vs On-Campus PhD: Key Differences

  • Affordability. An online PhD is more affordable compared to the traditional on-campus alternative. An on-campus PhD can cost as much as $30,000 per year, while an online PhD may be as low as $20,000 per year.
  • Flexibility. Online PhD students have the liberty to conduct in-depth study and research at their own time as opposed to the schedule of an in-person PhD program. Moreover, most online PhD programs don’t have an enrollment date, and some online PhD work is asynchronous, meaning students can take classes from anywhere at their convenience.
  • Quality. Traditionally acquired PhDs are thought to be superior to their online counterparts by some employers and academics, probably due to sentiment. However, the quality of an online PhD is dependent on the research subject, the school’s reputation, and accreditation.
  • Availability of funding. Funding available for online PhD programs may be limited due to some geographical constraints. For example, online PhD students cannot take up teaching assistantship positions unless they are willing to be physically present.

How to Get a PhD in Machine Learning Online: A Step-by-Step Guide

An online machine learning PhD student sitting at a coffee shop table, working on a computer.

To get a PhD in Machine Learning, you need to first apply online to a PhD program. If accepted, you must enroll in the required classes and complete the academic coursework, research, and a series of academic milestones, which include attaining candidacy, passing the qualification examinations, proposing, writing, and defending your dissertation.

To begin your journey to acquiring a PhD in Machine Learning, you first need to apply online to the school of your choice. You also need to fulfill the admission requirements, including possessing a master's or bachelor's degree–depending on the school–in a relevant field, a minimum grade point average, letters of recommendation, and GRE test scores . 

Many online PhD programs require students to take and pass a minimum number of credit hours in core and elective courses. A typical online PhD in Machine Learning program consists of about 70 to 90 credit hours that involve intensive research in a provided or chosen area of concentration. 

Obtaining a PhD in Machine Learning allows an individual to become a world-renowned expert in the field. After completing a rigorous course of study and passing a series of exams, the doctoral candidate would then undertake an original research project that contributes new knowledge to the field. Upon successful completion of the degree, the graduate would be able to pursue a career in academia or industry. 

Examinations are an essential part of any education. They test a student's understanding of the material and help them to learn and remember the information. If you want to earn a machine learning PhD, you must pass the examinations for various core and required courses. Then, you will need to complete and defend your dissertation.

A dissertation is a research paper that is submitted to and defended by a graduate student to earn a graduate degree. To graduate with a PhD in Machine Learning, you are required to write a dissertation on a topic related to machine learning. Your doctoral dissertation must demonstrate your knowledge and understanding of the field of machine learning, as well as your ability to conduct original research in the field.

Online PhD in Machine Learning Salary and Job Outlook

The job outlook for machine learning jobs is 22 percent between 2020 and 2030 , with the number of new jobs expected in this time frame being 7,200, according to the US Bureau of Labor Statistics. The average salary for computer and information research scientists, which is a category that machine learning professionals belong to, is $131,490 per year .

What Can You Do With an Online Doctorate in Machine Learning?

With an online doctorate in machine learning, you can qualify for specialization roles and lead machine learning positions, including senior machine learning engineer and computer research scientist.

Depending on your preferences, you may also opt for a research and academic career path to become a university professor. The list below is a list of the best jobs for PhD in Machine Learning graduates.

Best Jobs with a PhD in Machine Learning

  • Senior Machine Learning Engineer
  • Computer and Information Research Scientist
  • Data Scientist
  • Software Engineer
  • Postsecondary Teacher

Potential Careers With a Machine Learning Degree

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What Is the Average Salary for an Online PhD Holder in Machine Learning? 

The average salary for a PhD in Machine Learning holder is $108,000 per year , according to PayScale’s salary for skills in machine learning. The average salary a PhD holder receives depends on the location and position you apply for.

Highest-Paying Machine Learning Jobs for PhD Grads

Best machine learning jobs for online phd holders.

The best machine learning jobs for online PhD holders are typically high-paying jobs that require advanced-level skills that coincide with the nature of the position they undertake. Below are some typical job titles that online machine learning PhD degree holders assume.

A senior machine learning engineer oversees a team of machine engineers charged with designing and developing effective machine learning and deep learning solutions implemented in machine learning systems.

  • Salary with a Machine Learning PhD: $153,255
  • Job Outlook: 22% job growth from 2020 to 2030
  • Number of Jobs: 33,000
  • Highest-Paying States: Oregon, Arizona, Texas

Computer and information research scientists research and develop new ways of solving complex computing problems and apply existing technology. They work to significantly increase the knowledge in the field of computer science, which will aid in the production of more efficient software and hardware technologies.

  • Salary with a Machine Learning PhD: $131,490

A senior data scientist is responsible for developing data mining and machine learning techniques to solve complex business problems. They identify patterns and trends in large data sets, develop models to improve forecasting and decision making, and effectively communicate data-driven insights to non-technical stakeholders and lead a team of data analysts.

  • Salary with a Machine Learning PhD: $127,455

A software engineer is a professional that develops and maintains software. They work on a variety of software, from operating systems to video games, and may be involved in the development of websites. They must also have an excellent understanding of computer programming languages and be able to solve complex problems.

  • Salary with a Machine Learning PhD: $121,115
  • Number of Jobs: 1,847,900
  • Highest-Paying States: Washington, California, New York

Postsecondary teachers are in charge of lecturing students in colleges and universities. They are also responsible for instructing adults in several academic and non-academic subjects including career, work, and research.

  • Salary with a Machine Learning PhD: $79,640
  • Job Outlook: 12% job growth from 2020 to 2030
  • Number of Jobs: 1,276,900
  • Highest-Paying States: California, Oregon, District of Columbia

Is It Worth It to Do a PhD in Machine Learning Online?

Yes, it is worth it to do a PhD in Machine Learning online. Getting a PhD is not for everyone, as the process will require tremendous effort and discipline, but it can be rewarding. A PhD in Machine Learning online allows you to learn from some of the best minds in the field.

You can also specialize in an area of your choice, such as big data, natural language processing, or deep learning. Specializing in one area for your PhD in Machine Learning allows you to deep-dive into that subject and build doctorate-level expertise.

An online PhD in Machine Learning provides students with the same high-quality education as a traditional PhD but with more flexibility and affordability. You’ll have access to top-notch instructors, state-of-the-art technology, and a thriving online community of experts.

Additional Reading About Machine Learning

[query_class_embed] https://careerkarma.com/blog/machine-learning/ https://careerkarma.com/blog/best-machine-learning-bachelors-degrees/ https://careerkarma.com/blog/best-machine-learning-masters-degrees/

Online PhD in Machine Learning FAQ

Yes, you should get an online PhD in Machine Learning if it is critical for your career prospects. An online PhD in Machine Learning allows you to learn at your own pace and keep your day job while you pursue your degree. In the end, it sets you up for the highest-earning jobs in the machine learning industry , with better pay and a larger professional network.

The type of research you will carry out as a machine learning student includes research in deep learning, neural networks , machine learning algorithms, supervised and unsupervised machine learning, predictive learning, and computer vision. Students will make use of quantitative and experimental methods of research as well as the use of optimal feature selection.

You can choose a concentration for an online machine learning PhD by factoring in your interests, strengths, and career goals. You may also consider recent trends, the average salary of machine learning professionals , or the career options the machine learning industry has to offer when choosing a machine learning concentration.

Examples of online machine learning PhD dissertations include experimental quantum speed-up in reinforcement learning agents, improving automated medical diagnosis systems with machine learning technologies, regulating deep learning and robotics, and the use of machines and robotics in medical procedures.

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Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

  • Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
  • Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
  • Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
  • Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
  • Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
  • Foster economic development in the state of Georgia.
  • Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

  • Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
  • Area electives (5 courses, 15 hours).
  • Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
  • Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
  • Doctoral minor (2 courses, 6 hours).
  • Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
  • PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

  • ISYE 6412 , Theoretical Statistics
  • ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
  • MATH 7251 High Dimension Probability
  • MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

  • CS 7545 Machine Learning Theory and Methods
  • CS 7616 , Pattern Recognition
  • CSE 6740 / ISYE 6740 , Computational Data Analysis
  • ECE 6254 , Statistical Machine Learning
  • ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

  • ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
  • ISYE 6661 , Linear Optimization
  • ISYE 6663 , Nonlinear Optimization
  • ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

  • AE 6505 , Kalman Filtering
  • BMED 6700 , Biostatistics
  • ECE 6558 , Stochastic Systems
  • ECE 6601 , Random Processes
  • ECE 6605 , Information Theory
  • ISYE 6404 , Nonparametric Data Analysis
  • ISYE 6413 , Design and Analysis of Experiments
  • ISYE 6414 , Regression Analysis
  • ISYE 6416 , Computational Statistics
  • ISYE 6420 , Bayesian Statistics
  • ISYE 6761 , Stochastic Processes I
  • ISYE 6762 , Stochastic Processes II
  • ISYE 7400 , Adv Design-Experiments
  • ISYE 7401 , Adv Statistical Modeling
  • ISYE 7405 , Multivariate Data Analysis
  • ISYE 8803 , Statistical and Probabilistic Methods for Data Science
  • ISYE 8813 , Special Topics in Data Science
  • MATH 6263 , Testing Statistical Hypotheses
  • MATH 6266 , Statistical Linear Modeling
  • MATH 6267 , Multivariate Statistical Analysis
  • MATH 7244 , Stochastic Processes and Stochastic Calculus I
  • MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

  • CS 7280 , Network Science
  • CS 7510 , Graph Algorithms
  • CS 7520 , Approximation Algorithms
  • CS 7530 , Randomized Algorithms
  • CS 7535 , Markov Chain Monte Carlo Algorithms
  • CS 7540 , Spectral Algorithms
  • CS 8803 , Continuous Algorithms
  • ECE 6283 , Harmonic Analysis and Signal Processing
  • ECE 6555 , Linear Estimation
  • ISYE 7682 , Convexity
  • MATH 6112 , Advanced Linear Algebra
  • MATH 6221 , Advanced Classical Probability Theory
  • MATH 6241 , Probability I
  • MATH 6580 , Introduction to Hilbert Space
  • MATH 7338 , Functional Analysis
  • MATH 7586 , Tensor Analysis
  • MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

  • AE 6373 , Advanced Design Methods
  • AE 8803 , Machine Learning for Control Systems
  • AE 8803 , Nonlinear Stochastic Optimal Control
  • BMED 6780 , Medical Image Processing
  • BMED 8813 BHI, Biomedical and Health Informatics
  • BMED 8813 MHI, mHealth Informatics
  • BMED 8813 MLB, Machine Learning in Biomedicine
  • BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
  • CS 6440 , Introduction to Health Informatics
  • CS 6465 , Computational Journalism
  • CS 6471 , Computational Social Science
  • CS 6474 , Social Computing
  • CS 6475 , Computational Photography
  • CS 6476 , Computer Vision
  • CS 6601 , Artificial Intelligence
  • CS 7450 , Information Visualization
  • CS 7476 , Advanced Computer Vision
  • CS 7630 , Autonomous Robots
  • CS 7632 , Game AI
  • CS 7636 , Computational Perception
  • CS 7643 , Deep Learning
  • CS 7646 , Machine Learning for Trading
  • CS 7650 , Natural Language Processing
  • CSE 6141 , Massive Graph Analysis
  • CSE 6240 , Web Search and Text Mining
  • CSE 6242 , Data and Visual Analytics
  • CSE 6301 , Algorithms in Bioinformatics and Computational Biology
  • ECE 4580 , Computational Computer Vision
  • ECE 6255 , Digital Processing of Speech Signals
  • ECE 6258 , Digital Image Processing
  • ECE 6260 , Data Compression and Modeling
  • ECE 6273 , Methods of Pattern Recognition with Application to Voice
  • ECE 6550 , Linear Systems and Controls
  • ECE 8813 , Network Security
  • ISYE 6421 , Biostatistics
  • ISYE 6810 , Systems Monitoring and Prognosis
  • ISYE 7201 , Production Systems
  • ISYE 7204 , Info Prod & Ser Sys
  • ISYE 7203 , Logistics Systems
  • ISYE 8813 , Supply Chain Inventory Theory
  • HS 6000 , Healthcare Delivery
  • MATH 6759 , Stochastic Processes in Finance
  • MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

  • CS 6515 , Introduction to Graduate Algorithms
  • CS 6550 , Design and Analysis of Algorithms
  • CSE 6140 , Computational Science and Engineering Algorithms
  • CSE 6643 , Numerical Linear Algebra
  • CSE 6644 , Iterative Methods for Systems of Equations
  • CSE 6710 , Numerical Methods I
  • CSE 6711 , Numerical Methods II
  • ISYE 6644 , Simulation
  • ISYE 6645 , Monte Carlo Methods
  • ISYE 6662 , Discrete Optimization
  • ISYE 6664 , Stochastic Optimization
  • ISYE 6679 , Computational methods for optimization
  • ISYE 7686 , Advanced Combinatorial Optimization
  • ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

  • CS 6421 , Temporal, Spatial, and Active Databases
  • CS 6430 , Parallel and Distributed Databases
  • CS 6290 , High-Performance Computer Architecture
  • CSE 6220 , High Performance Computing
  • CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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Carnegie Mellon University School of Computer Science

Machine learning department.

best phd programs for machine learning

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

Visit the Website

  • Back to Doctoral Programs

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College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

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  • PhD Program

The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of nine participating home schools:

  • Contact SCS
  • Contact CSE
  • Contact ChBE
  • Contact BME
  • Contact ECE
  • Contact ISYE
  • ​​​​​​​ Contact MATH

Application requirements and deadlines follow the same as that of the home unit an applicant is applying through. For example, ML PhD applicants to the ECE home unit follow the same rules as the PhD ECE application requirements and deadlines. 

External applications are only accepted for the Fall semester each year.  The application deadline varies by home school with the earliest deadline of December 1. Most home schools have a final deadline of December 15. Check with home schools above for more specific details. 

Click here for application information and to apply  

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

Note most home units have made the GRE optional for fall 2023 applications. Contact the home unit at the above links for any specific info. 

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships) are determined by the home units. Please review the home unit links above or contact them directly for details.

Have Questions?

Please contact the above  home units directly for questions related to:.

  • Application deadlines
  • Application fee waivers
  • Assistantship/fellowship opportunities
  • Program fit
  • Advising Matching
  • GRE requirements - Many units have made this test optional. 
  • TOEFL minimum requirements and TOEFL waivers are determined by the GT Graduate Education Office:  https://grad.gatech.edu/english-proficiency . Note home units may required higher scores. 
  • Desired content in Statement of Purpose and Recommendation Letters

For technical application questions, please contact  [email protected]

  • Creating or using an account login
  • Forgotten password
  • Uploading documents 
  • Difficulty with recommender emails
  • How to access application status information (including application checklist)
  • Difficulty with the touchnet payment system

For general inquiries about curriculum or program requirements, please see FAQs or contact [email protected] .

Georgia Tech Transfer Students

If you are already enrolled in a Ph.D. program in one of the nine participating schools noted above, you may apply to the ML Ph.D. program as a transfer student.  You will be subject to the standard ML curriculum and qualifying requirements, so this is recommended only for graduate students in their first or second year.  

Potential transfer students must have a ML PhD Program thesis adviso r  who is willing to support them on a research assistantship. For more information, please email [email protected] .

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Computer Science, Ph.D.

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We have a thriving Ph.D. program with approximately 80 full-time Ph.D. students hailing from all corners of the world. Most full-time Ph.D. students have scholarships that cover tuition and provide a monthly stipend. Admission is highly competitive. We seek creative, articulate students with undergraduate and master's degrees from top universities worldwide. Our  current research strengths  include data management and analysis, cybersecurity, computer games, visualization, web search, graphics, vision and image processing, and theoretical computer science.

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View the Computer Science Ph.D. program flyer

Find out more about  Admission Requirements .

Note: for pre-fall 2015 Ph.D. students, please see the pre-fall 2015 Ph.D. Curriculum.

To receive a Ph.D. in Computer Science at the NYU Tandon School of Engineering, a student must:

  • satisfy a breadth course requirement, intended to ensure broad knowledge of computer science,
  • satisfy a depth requirement, consisting of an oral qualifying exam presentation with a written report, to ensure the student's ability to do research,
  • submit a written thesis proposal and make an oral presentation about the proposal,
  • write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and
  • satisfy all School of Engineering requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including graduate study duration, credit points, GPA, and time-to-degree requirements.

Upon entering the program, each student will be assigned an advisor who will guide them in formulating an individual study plan directing their course choice for the first two years. The department will hold an annual Ph.D. Student Assessment Meeting, in which all Ph.D. students will be formally reviewed.

In order to obtain a Ph.D. degree, a student must complete a minimum of 75 credits of graduate work beyond the BS degree, including at least 21 credits of dissertation. A Master of Science in Computer Science may be transferred as 30 credits without taking individual courses into consideration. Other graduate coursework in Computer Science may be transferred on a course-by-course basis. Graduate coursework in areas other than Computer Science can be transferred on a course-by-course basis with approval of the Ph.D. Committee (PHDC). The School of Engineering places some limits on the number and types of transfer credits that are available. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. Further details can be found in  the School of Engineering bulletin.

Each incoming Ph.D. student will be assigned to a research advisor, or to an interim advisor, who will provide academic advising until the student has a research advisor. The advisor will meet with the student when the student enters the program to guide the student in formulating an Individual Study Plan. The purpose of the plan is to guide the student’s course choice for the first two years in the program and to ensure that the student meets the breadth requirements. The plan may also specify additional courses to be taken by the student in order to acquire necessary background and expertise. Subsequent changes to the plan must be approved by the advisor.

Each Ph.D. student must complete a breadth requirement consisting of 6 courses. To remain in good academic standing, students must fulfill the breadth requirement within 24 months of entering the Ph.D. program.

Students who do not fulfill the breadth requirement within 24 months will be dismissed from the program unless an exception is granted by the PHDC. The PHDC will consult with the student’s research advisor to decide whether an exception is granted and to determine the conditions the student needs to meet.

Details of Breadth Requirement

The courses used to fulfill the breadth requirement must satisfy the following:

(a) Approved list courses:  At least 4 of the courses must be taken from the approved list of courses; see below "CS Ph.D. Breadth Requirement: Approved List of Courses." The 4 courses must satisfy the following two requirements:

i) Theory requirement:  At least one of the 4 courses must be taken in the Theory area.

ii) Systems & Applications Requirement:  At least two of the 4 courses must be taken in Systems & Applications.

Exemptions from approved list courses:  With the approval of the Ph.D. Committee, students who have previously received a grade of A or A- in a course that is on the approved list, while enrolled in another NYU graduate program, can use that course towards the breadth requirement in lieu of taking it while in the Ph.D. program.  Also, students who have previously received a grade of A or A- in a graduate course similar to one on the approved list, while enrolled in a graduate program at another university with standards comparable to those at NYU, can use that course in lieu of taking the course on the approved list. The determination of whether a course previously taken at another university can be used in this way will be made by the PHDC.

Approved Course List:  The list of approved courses will be reviewed regularly by the PHDC and is subject to change. Any changes must be approved by the CSE Department. In order for a course to be considered for inclusion in the list, the course must be rigorous and the students in it must be evaluated individually. Examples of inappropriate courses include those in which students are traditionally not differentially evaluated (e.g., all students receive A's or "pass") and courses in which grades are based on attendance or making a presentation of someone else's work, rather than on tests and assignments. Students, under their advisors’ guidance, should select their courses from the approved list so that they are exposed to a broad set of topics in computer science.

(b) Free choice courses:  Students must take 2 free choice courses in addition to the 4 required courses from the approved list. Students can use any graduate course at NYU as free choice courses but must obtain advisor approval to use a course not on the approved list. Students cannot use independent study courses (such as Advanced Project CS-GY 9963 or Readings in Computer Science, CS-GY 9413 and CS-GY 9423) or dissertation. Both free choice courses must be taken while in the CS Ph.D. program. No exemptions are available for free choice courses.

(c) GPA requirement:  Students must receive a grade of at least B in each of the six courses used to fulfill the breadth requirement. The average in the 4 approved list courses used to fulfill the breadth requirement must be at least 3.5. (For students who receive exemptions allowing them to take fewer than 4 approved list courses while in the CS PhD program, the average will be calculated over the approved list courses that were taken while in the CS Ph.D. program.) The average in the 2 free choice courses must also be at least 3.5.

(d) Requirement for Students who have never taken an Algorithms Course:  Any student who has not taken a course in Algorithms prior to entering the Ph.D. program, at either the undergraduate or the graduate level, must take a graduate course in algorithms while in the Ph.D. program. Students may take CS-GY 6033 (Design and Analysis of Algorithms I), CS-GY 6043 (Design and Analysis of Algorithms II), or CSCI-GA.3520 (Honors Analysis of Algorithms) to fulfill this requirement. The department may revise this list in the future depending on course offerings. Alternatively, students may petition the PHDC to use another course. The grade received in the course must be at least B.

By the end of a student’s third semester (throughout this document, the word “semester” is used to refer to fall or spring semester) in the program, at the latest, the student must be involved in a research project under the guidance of a faculty research advisor. It is the responsibility of each student to find a faculty advisor and a research project, and to inform the PHDC Chair about his/her choice of advisor. Students must inform the PHDC chair if they change their research advisor.

To satisfy the depth requirement, students must take a qualifying exam (QE) based on their research. The QE must be taken before the start of the student’s fifth semester in the program. Students are required to form a QE committee, select an exam topic, and a tentative date approved by the advisor and committee, by the end of their third semester.

The QE committee must consist of the advisor and at least two other members. The committee must be approved by the advisor and the PHDC. The advisor is the designated chair of the committee. All members of the QE committee must be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least two of the QE committee members must be tenured or tenure-track members of the CSE department unless permission is obtained from the PHDC to include only one such member.

For the QE, the student must give an oral presentation of her/his research accomplishments to the QE committee and write a detailed document describing those accomplishments. The document must be submitted to the QE committee and the PHDC no later than one week before the oral presentation. A student is expected to have conducted original research by the time of the exam. This research may have been carried out independently or in collaboration with faculty, research staff, or other students. Students are encouraged, but not required, to have publication-worthy results by the time of the exam. It is not sufficient for a student to present a survey of previous work in an area or a reimplementation of algorithms, techniques, or systems developed by others.

The committee, by majority vote, gives a grade for the exam as either "Pass" or "Fail. "  The chair of the QE committee will send this grade in writing to the student and to the PHDC chair, together with a written evaluation of the student's performance, approved by the QE committee members. A student who does not receive a “Ph.D. pass” may request permission from the PHDC to retake the exam. The PHDC will consult with the QE committee, review the case and make the final decision as to whether a retake is allowed or not. A student may petition the PHDC to change one or more members of the QE committee, but approval of the request will be at the PHDC’s discretion.

If the request for a retake is approved, the QE committee will determine the date for the second attempt. If the student is not allowed to retake the exam, the student will not be allowed to continue in the Ph.D. program in the following semester. If the student does not pass the qualifying exam on the second attempt, or otherwise does not satisfy the conditions given to her/him upon failing the exam the first time, the student will not be allowed to continue in the Ph.D. program in the following semester.

If a student has passed the QE and then changes his/her area of research, the student need not retake the QE.

Part-time students can petition the PHDC for extensions to the deadlines associated with the qualifying exam. Extensions should be for at most 2 semesters, except in extraordinary cases. Approval of extensions is at the discretion of the PHDC.

Within 6 months of passing the QE, each student is required to form a dissertation guidance committee. This committee must be approved by the student’s research advisor and by the PHDC. The committee must include at least four members. The committee members can be CSE faculty, faculty from other departments at NYU, or individuals of like standing from outside the university. At least one member of the dissertation guidance committee must be a tenured or tenure-track CSE faculty member, and at least one member of the committee must be from outside the CSE department.

By the end of the student’s fifth semester in the program, the student and committee must set a tentative date for the thesis proposal presentation. The presentation must be done prior to the start of the student’s seventh semester in the program.

Before finalizing the date of the presentation, the student must submit a written thesis proposal to the dissertation guidance committee which should include:

  • a description of the research topic
  • an explanation of how the research will advance the state of the art, and
  • a tentative research plan

After the dissertation guidance committee has approved the thesis proposal, the student should schedule the thesis proposal presentation and notify the PHDC chair once this has been finalized. The presentation should be announced to the faculty by the PHDC chair at least one week before it occurs. The presentation is open to all faculty. It may also be open to others at the discretion of the research advisor.

Substantial subsequent changes to the thesis topic must be approved by the dissertation guidance committee.

The last and most substantial aspect of the Ph.D. program is the dissertation. The research for the dissertation should be conducted in close consultation with the research advisor. When the adviser determines that the student is ready to defend the thesis, a dissertation defense will be scheduled. For the defense, the student will give an oral presentation describing the thesis research, which is open to the public. Following the oral presentation and an initial question and answer session, the dissertation committee and CSE faculty may ask the student further questions in closed session.

Other requirements for the Ph.D. dissertation and defense can be obtained from the Office of the Associate Dean for Graduate Academics in the NYU School of Engineering.

All Ph.D. students will be formally reviewed each year in a Ph.D. Student Assessment Meeting. The review is conducted by the entire CSE faculty and includes at least the following items (in no particular order):

  • All courses taken, grades received, and GPAs.
  • Research productivity: publications, talks, software, systems, etc.
  • Faculty input, especially from advisors and committee members.
  • Student’s own input.
  • Cumulative history of the student's progress.

As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:

  • In Good Standing: The student has performed well in the previous semester and may continue in the Ph.D. program for one more year, assuming satisfactory academic progress is maintained.
  • Not in Good Standing: The student has not performed sufficiently well in the previous year. The consequences of not being in good standing will vary, and may include being placed on probation, losing RA/GA/TA funding, or not being allowed to continue in the Ph.D. program.

Following the review, students will receive formal letters which will inform them of their standing. The letters may also make specific recommendations to the student as to what will be expected of them in the following year. A copy of each student’s letter will be placed in the student’s file.

Other School of Engineering requirements can be found in the  School of Engineering Bulletin.  Students must meet all applicable requirements, including graduate study duration, credit points, GPA, and time-to-degree requirements.

The following is the department policy concerning remote attendance at qualifying exams, dissertation proposal exams, and dissertation defenses, along with rules regarding the location and scheduling of these exams.

Any person attending an exam remotely must have a two-way video and audio connection.

1. Qualifying exams and proposal exams should be held at the Tandon campus in Brooklyn, except as indicated below.  It is preferable that all committee members be present in person.  However, in cases where attendance in person would be difficult, committee members other than the advisor are allowed to attend remotely. The advisor may attend remotely only with the permission of the PHDC.

If a Ph.D. student is working with a research advisor at an NYU campus outside of the United States, and both student and advisor are at that campus at the time of the qualifying exam, the student may take the exam on that campus with the advisor present.  The remaining members of the committee may attend remotely.

Any other arrangements must be approved by the PHDC.

2.  All dissertation defenses must take place on the Tandon campus.  Defenses should be held on a day in which the Tandon School of Engineering is open for business.  It is not a requirement that classes be in session.  Permission must be obtained from the PHDC to hold a dissertation defense on a weekend, or on a holiday or vacation day when the school is not open for business.

The student, research advisor, and any members of the committee who are on the CSE department faculty, should be present in person at the defense. If a member of the CSE department faculty who is on the committee is unable to attend in person, permission must be obtained from the PHDC for that person to attend remotely.  It is highly desirable for all other members of the committee to be present in person.  However, if it is difficult for other committee members to attend in person, they may attend remotely.

The following courses at NYU Tandon School of Engineering can be used to satisfy the breadth requirements:

3 Credits Design and Analysis of Algorithms II CS-GY 6043 This course covers techniques in advanced design and analysis. Topics: Amortized analysis of algorithms. Advanced data structures: binomial heaps, Fibonacci heaps, data structures for disjoint sets, analysis of union by rank with path compression. Graph algorithms: elementary graph algorithms, maximum flow, matching algorithms. Randomized algorithms. Theory of NPcompleteness and approach to finding (approximate) solutions to NPcomplete problems. Selected additional topics that may vary. Knowledge of algorithms and data structures equivalent to CS-GY 6033. Prerequisite: Graduate standing. 3 Credits Algorithmic Machine Learning and Data Science CS-GY 6763 This course gives a behind-the-scenes look into the algorithms and computational methods that make machine learning and data science work at large scale. How does a service like Shazam match a sound clip to a library of 10 million songs in under a second? How do scientists find patterns in terabytes of genetic data? How can we efficiently train neural networks with millions of parameters on millions of labeled images? We will address these questions and others by studying advanced algorithmic techniques like randomization, approximation, sketching, continuous optimization, spectral methods, and Fourier methods. Students will learn how to theoretically analyze and apply these techniques to problems in machine learning and data science. They will also have the opportunity to explore recent research in algorithms for data through a final project and optional reading group. This course is mathematically rigorous and is intended for graduate students or strong, advanced undergraduates. Knowledge of machine learning (equivalent to CS-UY 4563, CS-GY 6923, or ECE-GY 6143), algorithms (equivalent to CS-UY 2413, CS-GY 6033, or CS-GY 6043), and linear algebra (equivalent to MA-UY 2034, 3044, or 3054). Prerequisite: Graduate Standing. 3 Credits Theory of Computation CS-GY 6753 This course introduces the theory of computation. Topics: Formal languages and automata theory. Deterministic and non-deterministic finite automata, regular expressions, regular languages, context-free languages. Pumping theorems for regular and context-free languages. Turing machines, recognizable and decidable languages. Limits of computability: the Halting Problem, undecidable and unrecognizable languages, reductions to prove undecidability. Time complexity, P and NP, Cook-Levin theorem, NP completeness. Prerequisites: Graduate standing and CS-GY 6003 (or instructor's permission). Knowledge of discrete math (equivalent to CS-GY 6003). Prerequisite: Graduate Standing. 3 Credits Computational Geometry CS-GY 6703 This course introduces data structures and algorithms for geometric data. Topics include intersection, polygon triangulation, linear programming, orthogonal range searching, point location, Voronoi diagrams, Delaunay triangulations, arrangements and duality, geometric data structures, convex hulls, binary space partitions, robot motion planning, quadtrees, visibility graphs, simplex range searching. Knowledge of algorithms and data structures equivalent to CS-GY 6033. Prerequisite: Graduate Standing.

Systems and Applications

                    3 Credits Computer Architecture II CS-GY 6143 An overview of state-of-the-art single-core systems, including advanced pipelining, super-scalar, vector processors, VLIW and vector processing. High-performance computing systems: Computer systems that improve performance and capacity by exploiting parallelism. Selected topics in parallel computing are introduced, such as interconnection networks, parallel algorithms, GPUs, PRAMs, MIMD and SIMD machines. Alternatives to traditional computing are discussed, including GPUs, TPUs, systolic arrays, neural networks and experimental systems. Prerequisite: Graduate standing and CS-GY 6133. 3 Credits Operating Systems II CS-GY 6243 This course surveys recent important commercial and research trends in operating systems. Topics may include virtualization, network server design and characterization, scheduling and resource optimization, file systems, memory management, advanced debugging techniques, data-center design and energy utilization. Prerequisite: Graduate standing and CS-GY 6233. 3 Credits Distributed Operating Systems CS-GY 6253 This course introduces distributed-networked computer systems. Topics: Distributed control and consensus. Notions of time in distributed systems. Client/Server communications protocols. Middleware. Distributed File Systems and Services. Fault tolerance, replication and transparency. Peer-to-peer systems. Case studies of modern commercial systems and research efforts. 3 Credits Big Data CS-GY 6513 Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. In this course, we will study the state of art in big data management: we will learn about algorithms, techniques and tools needed to support big data processing. In addition, we will examine real applications that require massive data analysis and how they can be implemented on Big Data platforms. The course will consist of lectures based both on textbook material and scientific papers. It will include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data platforms, including Amazon AWS. Besides lectures given by the instructor, we will also have guest lectures by experts in some of the topics we will cover. Students should have experience in programming: Java, C, C++, Python, or similar languages, equivalent to two introductory courses in programming, such as ?Introduction to Programming? and ?Data Structures and Algorithms. Knowledge of Python. Prerequisite: Graduate Standing. 3 Credits Computer Networking CS-GY 6843 This course takes a top-down approach to computer networking. After an overview of computer networks and the Internet, the course covers the application layer, transport layer, network layer and link layers. Topics at the application layer include client-server architectures, P2P architectures, DNS and HTTP and Web applications. Topics at the transport layer include multiplexing, connectionless transport and UDP, principles or reliable data transfer, connection-oriented transport and TCP and TCP congestion control. Topics at the network layer include forwarding, router architecture, the IP protocol and routing protocols including OSPF and BGP. Topics at the link layer include multiple-access protocols, ALOHA, CSMA/CD, Ethernet, CSMA/CA, wireless 802.11 networks and linklayer switches. The course includes simple quantitative delay and throughput modeling, socket programming and network application development and Ethereal labs. Knowledge of Python and/or C. Prerequisite: Graduate standing. 3 Credits Network Security CS-GY 6823 This course begins by covering attacks and threats in computer networks, including network mapping, port scanning, sniffing, DoS, DDoS, reflection attacks, attacks on DNS and leveraging P2P deployments for attacks. The course continues with cryptography topics most relevant to secure networking protocols. Topics covered are block ciphers, stream ciphers, public key cryptography, RSA, Diffie Hellman, certification authorities, digital signatures and message integrity. After surveying basic cryptographic techniques, the course examines several secure networking protocols, including PGP, SSL, IPsec and wireless security protocols. The course examines operational security, including firewalls and intrusion-detection systems. Students read recent research papers on network security and participate in an important lab component that includes packet sniffing, network mapping, firewalls, SSL and IPsec. Prerequisite: Graduate standing. * Online version available. 3 Credits Principles of Database Systems CS-GY 6083 This course broadly introduces database systems, including the relational data model, query languages, database design, index and file structures, query processing and optimization, concurrency and recovery, transaction management and database design. Students acquire hands-on experience in working with database systems and in building web-accessible database applications. Knowledge of basic data structures and algorithms (search trees, hash tables, sorting and searching). Knowledge of principles of operating systems and of the client-server architecture. Basic familiarity with the UNIX operating systems. Programming proficiency. Prerequisites: Graduate standing. 3 Credits Compiler Design and Construction CS-GY 6413 This course covers compiler organization. Topics: Lexical analysis, syntax analysis, abstract syntax trees, symbol table organization, code generation. Introduction to code optimization techniques. Knowledge of discrete math equivalent to CS-GY 6003, and knowledge of fundamental data structures. Prerequisites: Graduate Standing. 3 Credits Interactive Computer Graphics CS-GY 6533 This course introduces the fundamentals of computer graphics with hands-on graphics programming experiences. Topics include graphics software and hardware, 2D line segment-scan conversion, 2D and 3D transformations, viewing, clipping, polygon-scan conversion, hidden surface removal, illumination and shading, compositing, texture mapping, ray tracing, radiosity and scientific visualization. Knowledge of Data Structures and Algorithms, and be comfortable with C/C++ programming.. Prerequisites: Graduate standing. 3 Credits Artificial Intelligence I CS-GY 6613 Artificial Intelligence (AI) is an important topic in computer science and offers many diversified applications. It addresses one of the ultimate puzzles humans are trying to solve: How is it possible for a slow, tiny brain, whether biological or electronic, to perceive, understand, predict and manipulate a world far larger and more complicated than itself? And how do people create a machine (or computer) with those properties? To that end, AI researchers try to understand how seeing, learning, remembering and reasoning can, or should, be done. This course introduces students to the many AI concepts and techniques. Knowledge of Data Structures and Algorithms. Prerequisite: Graduate standing. 3 Credits Application Security CS-GY 9163 This course addresses the design and implementation of secure applications. Concentration is on writing software programs that make it difficult for intruders to exploit security holes. The course emphasizes writing secure distributed programs in Java. The security ramifications of class, field and method visibility are emphasized. Knowledge of Information, Security and Privacy equivalent to CS-GY 6813. Prerequisite: Graduate standing 3 Credits Advanced Database Systems CS-GY 6093 Students in this advanced course on database systems and data management are assumed to have a solid background in databases. The course typically covers a selection from the following topics: (1) advanced relational query processing and optimization, (2) OLAP and data warehousing, (3) data mining, (4) stream databases and other emerging database architectures and applications, (5) advanced transaction processing, (6) databases and the Web: text, search and semistructured data, or (7) geographic information systems. Topics are taught based on a reading list of selected research papers. Students work on a course project and may have to present in class. Knowledge of Database Systems equivalent to CS-GY 6083 and experience with a relational database system. Prerequisites: Graduate standing. 3 Credits Computer Vision CS-GY 6643 An important goal of artificial intelligence (AI) is to equip computers with the capability of interpreting visual inputs. Computer vision is an area in AI that deals with the construction of explicit, meaningful descriptions of physical objects from images. It includes as parts many techniques from image processing, pattern recognition, geometric modeling, and cognitive processing. This course introduces students to the fundamental concepts and techniques in computer vision. Knowledge of Data Structures and Algorithms, proficiency in programming, and familiarity with matrix arithmetic. Prerequisites: Graduate standing. 3 Credits Web Search Engines CS-GY 6913 This course covers the basic technology underlying Web search engines and related tools. The main focus is on large-scale Web search engines (such as Google, Yahoo and MSN Search) and their underlying architectures and techniques. Students learn how search engines work and get hands-on experience in how to build search engines from the ground up. Topics are based on a reading list of recent research papers. Students must work on a course project and may have to present in class. Prerequisite: Graduate standing 3 Credits Machine Learning CS-GY 6923 This course is an introduction to the field of machine learning, covering fundamental techniques for classification, regression, dimensionality reduction, clustering, and model selection. A broad range of algorithms will be covered, such as linear and logistic regression, neural networks, deep learning, support vector machines, tree-based methods, expectation maximization, and principal components analysis. The course will include hands-on exercises with real data from different application areas (e.g. text, audio, images). Students will learn to train and validate machine learning models and analyze their performance. Knowledge of undergraduate level probability and statistics, linear algebra, and multi-variable calculus. Prerequisite: Graduate standing. 3 Credits Information Visualization CS-GY 6313 An introductory course on Information Visualization based on a modern and cohesive view of the area. Topics include visualization design, data principles, visual encoding principles, interaction principles, single/multiple view methods, item/attribute, attribute reduction methods, toolkits, and evaluation. Overviews and examples from state-of-the-art research will be provided. The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques. Prerequisite: Graduate Standing. 3 Credits Human Computer Interaction CS-GY 6543 Designing a successful interactive experience or software system takes more than technical savvy and vision--it also requires a deep understanding of how to serve people's needs and desires through the experience of the system, and knowledge about how to weave this understanding into the development process. This course introduces key topics and methods for creating and evaluating human-computer interfaces/digital user experiences. Students apply these practices to a system of their choosing (I encourage application to prototype systems that students are currently working on in other contexts, at any stage of development). The course builds toward a final write-up and presentation in which students detail how they tackled HCI/user experience design and evaluation of their system, and results from their investigations. Some experience creating/participating in the production of interactive experiences/software is recommended. Knowledge of the design of user experiences and interfaces is desirable but not required. Prerequisite: Graduate Standing. 3 Credits Game Design CS-GY 6553 This course is about experimental game design. Design in this context pertains to every aspect of the game, and these can be broadly characterized as the game system, control, visuals, audio, and resulting theme. We will explore these aspects through the creation of a few very focused game prototypes using a variety of contemporary game engines and frameworks, high-level programming languages, and physical materials. This will allow us to obtain a better understanding of what makes games appealing, and how game mechanics, systems, and a variety of player experiences can be designed and iteratively improved by means of rapid prototyping and play-testing. The course combines the technology, design, and philosophy in support of game creation, as well as the real-world implementation and design challenges faced by practicing game designers. Students will learn design guidelines and principles by which games can be conceived, prototyped, and fully developed within a one-semester course, and will create a game from start to finish. The course is a lot of (team)work, but it's also a lot of fun. Programming skills are helpful, but not a hard requirement. Artistic skills, or a willingness to learn them are a plus. Prerequisite: (Graduate Standing AND CS-GY 6533) for SoE students OR (OART-UT 1600 and OART-UT 1605) for Game Center MFA students OR instructor permission. 3 Credits Artificial Intelligence for Games CS-GY 6943 This course covers artificial intelligence techniques used with games. The course is an advanced course that presupposes a good understanding of standard AI techniques, and much of the course material will consists of recent research papers. While the course will cover recent methods for playing games, in particular for general game playing, it will also go beyond that application domain to cover methods for generating games and game content and for modeling players. Many of these methods are based on evolutionary computation, others on stochastic tree search, cellular automata or grammar expansion. Approximately the first half of the course will consist of lectures, and the second half of the group projects. Prerequisites: Graduate Standing and CS-GY 6613 or similar introductory Artificial Intelligence courses.

The following courses, offered the Computer Science Department at the Courant Institute of Mathematical Sciences at NYU, can also be used to satisfy the breadth requirements:

  • Honors Analysis of Algorithms  CSCI-GA.3520
  • High Performance Computer Architecture  CSCI-GA.2243
  • Networks and Distributed Systems  CSCI-GA.2620
  • Honors Programming Languages  CSCI-GA.3110
  • Honors Compilers  CSCI-GA.3130
  • Honors Operating Systems  CSCI-GA.3250
  • Computer Graphics  CSCI-GA.2270
  • Computer Vision  CSCI-GA.2271
  • Advanced Database Systems  CSCI-GA.2434
  • Artificial Intelligence  CSCI-GA.2560
  • Machine Learning  CSCI-GA.2565
  • Foundations of Machine Learning  CSCI-GA.2566
  • Natural Language Processing  CSCI-GA.2590

Quick Links

  • Graduate Admissions
  • Frequently Asked Questions
  • Pre-Fall 2015 Ph.D. Curriculum

Program Admissions Chair

Justin Cappos

Justin Cappos

Program director.

Rachel Greenstadt

Rachel Greenstadt

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Periwinkle Doerfler

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Nektarios Tsoutsos

Graduate Education

Machine learning, program contact.

Stephanie Niebuhr Georgia Institute of Technology 801 Atlantic Drive Atlanta, GA 30332-0405

Application Deadlines

Application deadline varies by home school.

  • Aerospace Engineering: April 1
  • Biomedical Engineering: December 1
  • Electrical and Computer Engineering: December 16
  • Industrial & Systems Engineering: December 15
  • Mathematics: December 15
  • School of Chemical & Biomolecular Engineering: December 15
  • School of Computational Science & Engineering: December 15
  • School of Computer Science: December 15
  • School of Interactive Computing: December 15

Admittance Terms

Degree programs.

  • PhD, Machine Learning

Areas of Research

Our world-class faculty and students specialize in areas including, but not limited to:

  • Computer Vision
  • Natural Language Processing
  • Deep Learning
  • Game Theory
  • Neuro Computing
  • Ethics and Fairness
  • Artificial Intelligence
  • Internet of Things
  • Machine Learning Theory
  • Systems for Machine Learning
  • Bioinformatics
  • Computational Finance
  • Health Systems
  • Information Security
  • Logistics and Manufacturing

Interdisciplinary Programs

The Machine Learning Ph.D. is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of eight participating home schools:

  • Computer Science (Computing)
  • Computational Science and Engineering (Computing)
  • Interactive Computing (Computing)– see  Computer Science
  • Aerospace Engineering (Engineering)
  • Biomedical Engineering (Engineering)
  • Electrical and Computer Engineering (Engineering)
  • Mathematics (Sciences)
  • Industrial Systems Engineering (Engineering)

Admission to the ML PhD program is contingent on meeting the requirement for admission into one of these schools. It is possible that, due to space or other constraints, that you are admitted to the general PhD program in your home school but not the ML PhD program.

The ML PhD program is a cohesive, interdisciplinary course of study subject to a unique set of curriculum requirements; see the program webpage for more information.

Standardized Tests

IELTS Academic Requirements

  • Varies among home units.

TOEFL Requirements

GRE Requirements

Application Requirements

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships). Please review the home unit links above or contact them directly for details.

Program Costs

  • Go to " View Tuition Costs by Semester ," and select the semester you plan to start. Graduate-level programs are divided into sections: Graduate Rates–Atlanta Campus, Study Abroad, Specialty Graduate Programs, Executive Education Programs
  • Find the degree and program you are interested in and click to access the program's tuition and fees by credit hour PDF.
  • In the first column, determine the number of hours (or credits) you intend to take for your first semester.
  • Determine if you will pay in-state or out-of-state tuition. Learn more about the difference between in-state and out-of-state . For example, if you are an in-state resident and planning to take six credits for the Master of Architecture degree, the tuition cost will be $4,518.
  • The middle section of the document lists all mandatory Institute fees. To see your total tuition plus mandatory fees, refer to the last two columns of the PDF.

Program Links

The Office of Graduate Education has prepared application instructions to help you navigate through the admissions process. View the instructions to begin your graduate education.

PhD in Artificial Intelligence Programs

Home » Artificial Intelligence Degree Hub » PhD in Artificial Intelligence Programs

Universities offer a variety of Doctor of Philosophy (Ph.D.) programs related to Artificial Intelligence (AI.) Some of these are titled as Ph.D.s in AI, whereas most are Ph.D.s in Computer Science or related engineering disciplines with a specialization or focus in AI. Admissions requirements usually include a related bachelor’s degree and, sometimes, a master’s degree. Moreover, most Ph.D. programs expect academic excellence and strong recommendations. The AI Ph.D. programs take three to five or more years, depending on if you have a master’s and the complexity of your dissertation. People with Ph.D.s in AI usually go on to tenure track professorships, postdoctoral research positions, or high-level software engineering positions.

What Are Artificial Intelligence Ph.D. Programs?

Ph.D. programs in AI focus on mastering advanced theoretical subjects, such as decision theory, algorithms, optimization, and stochastic processes. Artificial intelligence covers anything where a computer behaves, rationalizes, or learns like a human. Ph.D.s are usually the endpoint to a long educational career. By the time scholars earn Ph.D.s, they have probably been in school for well over 20 years.

People with an AI Ph.D. degree are capable of formulating and executing novel research into the subtopics of AI. Some of the subtopics include:

  • Environment adaptation in self-driving vehicles
  • Natural language processing in robotics
  • Cheating detection in higher education
  • Diagnosing and treating diseased in healthcare

AI Ph.D. programs require candidates to focus most of their coursework and research on AI topics. Most culminate in a dissertation of published research. Many AI Ph.D. recipients’ dissertations are published in peer-reviewed journals or presented at industry-leading conferences. They go on to lead careers as experts in AI technology.

Featured Schools

Types of artificial intelligence ph.d. programs.

Most AI Ph.D. programs are a Ph.D. in Computer Science with a concentration in AI. These degrees involve general, advanced level computer science courses for the first year or two and then specialize in AI courses and research for the remainder of the curriculum.

AI Ph.D.s offered in other colleges like Computer Engineering, Systems Engineering, Mechanical Engineering, or Electrical Engineering are similar to Ph.D.s in Computer Science. They often involve similar coursework and research. For instance, colleges like Indiana University Bloomington’s Computing and Engineering have departments specializing in AI or Intelligent Engineering. Some colleges, however, may focus more on a specific discipline. For example, a Ph.D. in Mechanical Engineering with an AI focus is more likely to involve electric vehicles than targeted online advertising.

Some AI programs fall under a Computational Linguistics specialization, like CUNY . These programs emphasize the natural language processing aspect of AI. Computational Linguistics programs still involve significant computer science and engineering but also require advanced knowledge in language and speech.

Other unique programs offer a joint Ph.D. with non-engineering disciplines, such as Carnegie Mellon’s Joint Ph.D. in Machine Learning and Public Policy, Statistics, or Neural Computation .

How Ph.D. in Artificial Intelligence Programs Work

Ph.D. programs usually take three to six years to complete. For example, Harvard lays out a three+ year track where the last year(s) is spent completing your research and defending your dissertation. Many Ph.D. programs have a residency requirement where you must take classes on-campus for one to three years. Moreover, most universities, such as Brandeis , require Ph.D. students to grade and/or teach for one to four semesters. Despite these requirements, several Ph.D. programs allow for part-time or full-time students, like Drexel .

Admissions Requirements

Ph.D. programs in AI admit the strongest students. Most applications require a resume, transcripts, letters of recommendation, and a statement of interest. Many programs require a minimum undergraduate GPA of 3.0 or higher, although some allow for statements of explanation if you have a lower GPA due to illness or other excusable causes for a low GPA.

Many universities, like Cornell , recently made the GRE either optional or not required because the GRE provides little prediction into the success of research and represents a COVID-19 risk. These programs may require the GRE again in the future. However, many schools still require the IELTS/TOEFL for international applicants.

Curriculum and Coursework

The curriculum for AI Ph.D.s varies based on the applicants’ prior education for many universities. Some programs allow applicants to receive credit for relevant master’s programs completed prior to admission. The programs require about 30 hours of advanced research and classes. Other programs do not give credit for master’s programs completed elsewhere. These require over 60 hours of electives, in addition to the 30-hours of fundamental and core classes in addition to the advanced courses.

For programs with more specific specialties, the courses are usually narrowly focused. For example, Duke’s Robotics track requires ten classes, at least three of which are focused on AI as it relates to robotics. Others allow for non-AI-specific courses such as computer networks.

Many Ph.D. programs have strict GPA requirements to remain in the program. For example, Northeastern requires PhD candidates to maintain at least a 3.5 GPA. Other programs automatically dismiss students with too many Cs in courses.

Common specializations include:

  • Computational Linguistics
  • Automotive Systems
  • Data Science

Artificial Intelligence Dissertations

Most Ph.D. programs require a dissertation. The dissertation takes at least two years to research and write, usually starting in the second or third year of the Ph.D. curriculum. Moreover, many programs require an oral presentation or defense of the dissertation. Some universities give an award for the best dissertation of the year. For example, Boston University gave a best dissertation award to Hao Chen for the dissertation entitled “ Improving Data Center Efficiency Through Smart Grid Integration and Intelligent Analytics .”

A couple of programs require publications, like Capitol Technology , or additional course electives, like LIU . For example, The Ohio State University requires 27 hours of graded coursework and three hours with an advisor for non-thesis path candidates. Thesis-path candidates only have to take 18 hours of graded coursework but must spend 12 hours with their advisors.

Are There Online Ph.D. in Artificial Intelligence Programs?

Officially, the majority of AI Ph.D. programs are in-person. Only one university, Capitol Technology University , allows for a fully online program. This is one of the most expensive Ph.D.s in the field, costing about $60,000. However, it is also one of the most flexible programs. It allows you to complete your coursework on your own schedule, perhaps even while working. Moreover, it allows for either a dissertation path or a publication path. The coursework is fully focused on AI research and writing, thus eliminating requirements for more general courses like algorithms or networks.

One detail you should consider is that the Capitol Technology Ph.D. program is heavily driven by a faculty mentor. This is someone you will need consistent contact with and open communication. The website only lists the director, so there is a significant element of uncertainty on how the program will work for you. But doctoral candidates who are self-driven and have a solid idea of their research path have a higher likelihood of succeeding.

If you need flexibility in your Ph.D. program, you may find some professors at traditional universities will work with you on how you meet and conduct the research, or you may find an alternative degree program that is online. Although a Ph.D. program may not be officially online, you may be able to spend just a semester or two on campus and then perform the rest of the Ph.D. requirements remotely. This is most likely possible if the university has an online master’s program where you can take classes. For example, the Georgia Institute of Technology does not have a residency requirement, has an online master’s of computer science program , and some professors will work flexibly with doctoral candidates with whom they have a close relationship.

What Jobs Can You Get with a Ph.D. in Artificial Intelligence?

Many Ph.D. graduates work as tenure track professors at universities with AI classes. Others work as postdoc research scientists at universities. Both of these roles are expected to conduct research and publish, but professors have more of an expectation to teach, as well. Universities usually have a small number of these positions available. Moreover, postdoc research positions tend to only last for a limited amount of time.

Other engineers with AI-focused-Ph.D.s conduct research and do software development in the private sector at AI-intensive companies. For example, Google uses AI in many departments. Its assistant uses natural language processing to interface with users through voice. Moreover, Google uses AI to generate news feeds for users. Google, and other industry leaders, have a strong preference for engineers with Ph.D.s. This career path is often highly sought by new Ph.D. recipients.

Another private sector industry shifting to AI is vehicle manufacturing. For example, self-driving cars use significant AI to make ethical and legal decisions while operating. Another example is that electric vehicles use AI techniques to optimize performance and power usage.

Some AI Ph.D. recipients become c-suite executives, such as Chief Technology Officers (CTO). For example, Dr. Ted Gaubert has a Ph.D. in engineering and works as a CTO for an AI-intensive company. Another CTO, Dr. David Talby , revolutionized AI with a new natural language processing library, Spark. CTO positions in AI-focused companies often have decades of experience in the AI field.

How Much Do Ph.D. in Artificial Intelligence Programs Cost?

The tuition for many Ph.D. programs is paid through fellowships, graduate research assistantships, and teaching assistantships. For example, Harvard provides full support for Ph.D. candidates. Some programs mandate teaching or research to attend based on the assumption that Ph.D. candidates need financial assistance.

Fellowships are often reserved for applicants with an exceptional academic and research background. These are usually named for eminent alumni, professors, or other scholars associated with the university. Receiving such a fellowship is a highly respected honor.

For programs that do not provide full assistance, the usual cost is about $500 to $1,000 per credit hour, plus university fees. On the low end, Northern Illinois University charges about $557 per credit hour . With 30 to 60 hours required, this means the programs cost about $30,000 to over $60,000 out of pocket. Typically, Ph.D. programs that do not provide funding for any Ph.D. candidates are less reputable or provide other benefits, such as flexibility, online programs, or fewer requirements.

How Much Does a Ph.D. in AI Make?

Engineers with AI Ph.D.s earn well into the six-figure range in the private sector. For example, OpenAI , a non-profit, pays its top researchers over $400,000 per year. Amazon pays its data scientists with Ph.D.s over $200,000 in salary. Directors and executives with Ph.D.s often earn over $1,000,000 in private industry.

When considering working in the private industry, professionals usually compare offers based on total compensation, not just salary. Many companies offer large stock and bonus packages to Ph.D.-level engineers and scientists.

Startups sometimes pay less in salary, but much more in stock options. For example, the salary may be $50,000 to $100,000, but when the startup goes public, you may end up with hundreds of thousands in stock options. This creates a sense of ownership and investment in the success of the startup.

Computer science professors and postdoctoral researchers earn about $90,000 to $160,000 from universities. However, they increase their competition by writing books, speaking at conferences, and advising companies. Startups often employ professors for advice on the feasibility and design of their technology.

Schools with PhD in Artificial Intelligence Programs

Arizona state university, ph.d. in computer science (artificial intelligence research).

Learn More:

  • Program Homepage
  • Admissions Info
  • Curriculum Info
  • Tuition Info

Ph.D. in Computing and Information Sciences (Artificial Intelligence Research)

Boston university, phd in computer engineering - data science and intelligent systems research area, phd in systems engineering - automation, robotics, and control, brandeis university, ph.d. in computer science - computational linguistics, capitol technology university, online doctor of philosophy (phd) in artificial intelligence, carnegie mellon university, phd in machine learning & public policy, phd in neural computation & machine learning, phd in statistics & machine learning, phd program in machine learning, colorado state university-fort collins, ph.d. in computer science - artificial intelligence research area, cornell university, linguistics ph.d. - computational linguistics, ph.d.in computer science, cuny graduate school and university center, ph.d. in linguistics - computational linguistics, drexel university, doctorate in mechanical engineering - robotics and autonomy, duke university, ph.d in ece - robotics track, ph.d. in mems - robotics track, georgetown university, doctor of philosophy (ph.d.) in linguistics - computational linguistics, georgia institute of technology, ph.d. in machine learning, harvard university, ph.d. in applied mathematics, indiana university bloomington, ph.d. in intelligent systems engineering, ph.d. in linguistics - computational linguistics concentration, johns hopkins university, doctor of philosophy in mechanical engineering - robotics, long island university-brooklyn campus, dual pharmd/m.s. in artificial intelligence, northeastern university, ph.d. in computer science - artificial intelligence area, northern illinois university, ph.d. in computer science - artificial intelligence area of emphasis, ph.d. in computer science - machine learning area of emphasis, northwestern university, phd in computer science - artificial intelligence and machine learning research group, ohio state university-main campus, phd in mechanical engineering - automotive systems and mobility (connected and automated vehicles), oregon state university, ph.d. in artificial intelligence, rochester institute of technology, rutgers university, ph.d. in linguistics with computational linguistics certificate, stevens institute of technology, ph.d. in computer engineering, ph.d. in electrical engineering - applied artificial intelligence, ph.d. in electrical engineering - robotics and smart systems research, temple university, phd in computer and information science - artificial intelligence, university of california-riverside, ph.d. in electrical engineering - intelligent systems research area, university of california-san diego, ph.d. in intelligent systems, robotics and control, university of central florida, doctorate in computer engineering - intelligent systems and machine learning, university of cincinnati, phd in computer science and engineering - intelligent systems group, university of colorado boulder, phd in robotics and systems design, university of michigan-ann arbor, phd in electrical and computer engineering - robotics, university of nebraska at omaha, phd in information technology - artificial intelligence concentration, university of nevada-reno, ph.d. in computer science & engineering - intelligent and autonomous systems research, university of pittsburgh-pittsburgh campus, ph.d. in intelligent systems, university of texas at austin, ph.d. with graduate portfolio program in robotics, university of texas at dallas, university of utah, doctor of philosophy - robotics track, university of washington - seattle, ph.d. in machine learning and big data, university of west florida, ph.d. in intelligent systems and robotics.

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Best Universities for Machine Learning in the World

Updated: July 18, 2023

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  • Engineering
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Below is a list of best universities in the World ranked based on their research performance in Machine Learning. A graph of 1.8B citations received by 83.2M academic papers made by 5,038 universities in the World was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Stanford University

For Machine Learning

Stanford University logo

2. University of California - Berkeley

University of California - Berkeley logo

3. University of Michigan - Ann Arbor

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4. Harvard University

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5. University of Toronto

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6. Carnegie Mellon University

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7. University of Washington - Seattle

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8. Massachusetts Institute of Technology

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9. Tsinghua University

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10. University of Illinois at Urbana - Champaign

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11. University of Oxford

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12. University of California - Los Angeles

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13. Nanyang Technological University

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14. University of Minnesota - Twin Cities

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15. Cornell University

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16. National University of Singapore

National University of Singapore logo

17. University of Pennsylvania

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18. Pennsylvania State University

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19. University of Wisconsin - Madison

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20. University of California-San Diego

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21. University of Cambridge

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22. Columbia University

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23. University of Texas at Austin

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24. University of Southern California

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25. Shanghai Jiao Tong University

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26. Ohio State University

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27. Yale University

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28. University College London

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29. New York University

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30. Georgia Institute of Technology

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31. Princeton University

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32. University of North Carolina at Chapel Hill

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33. Imperial College London

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34. University of Maryland - College Park

University of Maryland - College Park logo

35. University of British Columbia

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36. University of Chicago

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37. Chinese University of Hong Kong

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38. Catholic University of Leuven

Catholic University of Leuven logo

39. Johns Hopkins University

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40. Harbin Institute of Technology

Harbin Institute of Technology logo

41. Arizona State University - Tempe

Arizona State University - Tempe logo

42. Boston University

Boston University logo

43. Michigan State University

Michigan State University logo

44. Huazhong University of Science and Technology

Huazhong University of Science and Technology logo

45. Zhejiang University

Zhejiang University logo

46. University of Sydney

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47. Duke University

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48. Texas A&M University - College Station

Texas A&M University - College Station logo

49. University of Florida

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50. University of Alberta

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51. Technical University of Munich

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52. University of Melbourne

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53. University of New South Wales

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54. Purdue University

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55. University of Tokyo

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56. Xi'an Jiaotong University

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57. University of Pittsburgh

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58. University of Edinburgh

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59. University of Waterloo

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60. Rutgers University - New Brunswick

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61. Peking University

Peking University logo

62. Federal Institute of Technology Lausanne

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63. National Taiwan University

National Taiwan University logo

64. Beihang University

Beihang University logo

65. University of Manchester

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66. University of Electronic Science and Technology of China

University of Electronic Science and Technology of China logo

67. Swiss Federal Institute of Technology Zurich

Swiss Federal Institute of Technology Zurich logo

68. University of California - Davis

University of California - Davis logo

69. McGill University

McGill University logo

70. Hong Kong Polytechnic University

Hong Kong Polytechnic University logo

71. University of California - Irvine

University of California - Irvine logo

72. University of Queensland

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73. Seoul National University

Seoul National University logo

74. University of Amsterdam

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75. Hong Kong University of Science and Technology

Hong Kong University of Science and Technology logo

76. Delft University of Technology

Delft University of Technology logo

77. University of Montreal

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78. Southeast University

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79. Iowa State University

Iowa State University logo

80. University of Arizona

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81. University of Sheffield

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82. Monash University

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83. City University of Hong Kong

City University of Hong Kong logo

84. Virginia Polytechnic Institute and State University

Virginia Polytechnic Institute and State University logo

85. University of Sao Paulo

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86. Wuhan University

Wuhan University logo

87. University of California - San Francisco

University of California - San Francisco logo

88. Northwestern University

Northwestern University logo

89. California Institute of Technology

California Institute of Technology logo

90. Northwestern Polytechnical University

Northwestern Polytechnical University logo

91. North Carolina State University at Raleigh

North Carolina State University at Raleigh logo

92. Tel Aviv University

Tel Aviv University logo

93. Xidian University

Xidian University logo

94. University of Massachusetts - Amherst

University of Massachusetts - Amherst logo

95. Indian Institute of Technology Kanpur

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96. University of Science and Technology of China

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97. University of Bristol

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98. University of Copenhagen

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99. Central South University

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100. Australian National University

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Computer Science subfields in the World

  • Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Machine Learning - CMU

Machine learning academics.

The Machine Learning Department is made up of a multi-disciplinary team of faculty and students across several academic departments. Machine learning is dedicated to furthering the scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision making based on that understanding.

Today's demand for expertise in machine learning far exceeds the supply, and this imbalance will become more severe over the coming decade.

Students can pursue one of four Ph.D. programs, a Master's program, and an undergraduate Minor, Concentration, or Major. Students can also take classes in the Machine Learning Department without being part of one of its academic programs. For questions and concerns, please contact us .

We do NOT offer any online or part-time degrees, all of our programs are a full-time commitment at the Pittsburgh Campus.

PhD Programs

Phd in machine learning.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning and Computational Statistics. The program is operated jointly by faculty in the School of Computer Science and Department of Statistics.

We also offer Joint PhD Programs in Statistics & Machine Learning, Machine Learning & Public Policy, Neural Computation & Machine Learning

MS Curriculum and Program Requirements

The MS in Machine Learning is ideal for students considering a career in industry or as preparation for a PhD. Regardless of the application used, the curriculum and program requirements are the same.

Primary Application Information: MS in Machine Learning

The primary application is open to those who are not currently earning a degree from or working at CMU, as well as CMU members who prefer it over the Fifth-Year and Secondary MS.

Primary Application Information: MS in Machine Learning - Applied Study

The primary application is open to those who are not currently earning a degree from or working at CMU, as well as CMU members who prefer it over the Fifth-Year and Secondary MS. Unlike the other links in this list, the MS in Machine Learning - Applied Study is a unique degree for students planning a career in industry.

Fifth-Year MS in Machine Learning Application Information

Current CMU undergraduates may be eligible to apply early and earn the MS in their fifth year.

Secondary MS in ML Discontinued

Discontinued, secondary ms in machine learning application information, undergraduate programs, machine learning minor.

Machine learning and statistical methods are increasingly used in many application areas including natural language processing, speech, vision, robotics, and computational biology. The Minor in Machine Learning allows undergraduates to learn about the core principles of machine learning.

The curriculum varies based on when students began their undergraduate program at CMU:

Curriculum for 2018 and earlier Curriculum for 2019 and later

Machine Learning Concentration

Students within the School of Computer Science can add the Machine Learning Concentration to their major to enhance their computer science education.

Statistics & Machine Learning Major

Bachelor's of science in artificial intelligence, comparing ml intro courses, course comparison information, self assessment to determine if you should take 10601 or 10701., teaching assistantships, verification of skills.

Effective July 31, 2023

The university will no longer produce a letter that states actual skills, enumerated skills or anything not listed in the description. Additionally, a cademic departments and faculty may not produce skill verification letters.  The official course syllabus verifies the skills taught in the course and your official transcript verifies the successful completion of the course/skills.

Please see instructions from Enrollment Services: https://www.cmu.edu/hub/registrar/student-records/verifications/course.html

best phd programs for machine learning

Best Master’s in Data Science Programs for 2024: UCI Ranks #6

Uci moves up a spot on fortune’s list of the top master’s degree programs in data science..

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#6 Best in Master's in Data Science, Fortune 2024

“Data scientist” is the third fastest growing occupation , according to the U.S. Bureau of Labor Statistic, with a median pay of $103,500 per year. “Data science is more popular than ever, and there’s no sign the industry’s growth is slowing down anytime soon,” reads a Fortune article explaining the methodology behind its 2024 ranking of the top in-person master’s degree programs in data science. The article goes on to explain the three components of its ranking: selectivity, success, and demand.

Coming in at #6 is UC Irvine’s Master of Data Science ( MDS ) professional graduate program, offered through the Donald Bren School of Information and Computer Sciences ( ICS ). The program, which welcome its first cohort in 2021, moved up a spot from Fortune’s inaugural 2022 ranking and remains the only UC school on the list.

“Our program has an ambitious goal of being first in class for data science — not only on the curriculum side but also in terms of being integrated within the local orange county ecosystem,” says MDS Program Director Bryan Muñoz . “We want to be the epicenter for tech talent.”

Designed for High Impact The program combines a solid curriculum with practical experience and professional development. With pioneering faculty from the Department of Statistics and the Department of Computer Science , both housed in the School of ICS, students receive hands-on training in applied probability and mathematical statistics, statistical modeling and computing, machine learning, data management and visualization, and artificial intelligence. Then they apply those skills in two capstone project courses, addressing real-world problems, and receive one-on-one support from the MDS career services team.

“Being part of this program has really opened my eyes regarding the vast applications of data science,” says Sebastian Algharaballi-Yanow , a student in the current cohort serving as an MDS program ambassador. “Our balanced and relevant curriculum, along with stellar support from program management, has led me down a pathway of exploration and growth that has been simply invaluable. I’ve never felt more prepared to make an immediate impact in the industry.”

Fellow student and program ambassador Pranav Agarwal agrees. “MDS @ UCI has effectively bridged the divide between computer science and data science through its comprehensive curriculum and well-designed syllabus, which swiftly covers statistical concepts applicable in everyday life,” he says. “The program’s ranking speaks to its wealth of resources and opportunities.”

Students standing in front of a wall at UCI with artwork portraying two strong arms. The students are raising their arms as well, showing their strength.

A Program for Change Only in its third year, the program is experiencing remarkable growth, welcoming its largest cohort yet.

“The last three years have been extremely rewarding for our program — from the initial ranking to this new ranking — we continue to grow,” says Bin Nan , Chancellor’s Professor in Statistics and MDS Faculty Director. “We have spent our effort focusing on offering the most rigorous and industry-forward curriculum, something our Statistics and Computer Science Departments play a pivotal role in.”

Yet the focus isn’t only on growth. “While our cohorts have grown, we also focus on the social challenge of the tech landscape by being a program for change,” stresses Muñoz. For example, through its capstone project course, the program has supported Advance OC , a local nonprofit working to address inequities throughout Orange County. The program also supports underrepresented students with its Empowering Diversity Scholarship and Veteran’s Scholarship. “MDS is a changemaker, and we will continue the work to produce the best talent from around the world.”

— Shani Murray

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How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

best phd programs for machine learning

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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