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Material processes and systems @ harvard gsd, open research position: data science / machine learning for design.

Location: Cambridge MA (remote option is possible but must have a US work visa/authorization) Position Type: Part-time, Temporary Compensation: part-time 10 – 20 hours/week, hourly rate dependent on experience. Duration: Fall 2024 Job Description: We are seeking a highly motivated and talented graduate student with expertise in Data Science and Machine Learning to join our research team for a short-term research project. The primary focus of this position is to develop predictive models that can forecast preferences and ratings based on image data, in the context of an on-going design study on the aesthetic perception of wall and floor tiles. This role will provide an excellent opportunity to apply advanced machine learning techniques to a practical problem while contributing to cutting-edge research. This work is part of the Laboratory of Design Technologies and the Material Processes and Systems Group at the Harvard Graduate School of Design. Key Responsibilities: • Model Development: Design and implement machine learning models to predict user preferences and ratings from image data. • Data Processing: Clean, preprocess, and augment mid-size datasets of images to prepare them for model training and evaluation. • Model Training and Evaluation: Train, validate, and tune models to ensure high accuracy and robustness. Conduct performance evaluations using appropriate metrics. • Research Documentation: Document methodologies, experiments, and results in a clear and concise manner for both internal use and potential publication. • Collaboration: Work closely with interdisciplinary team members including designers, architects, industry experts, and supervisors to refine models and achieve research goals. • Literature Review: Stay updated with recent advancements and trends in machine learning and related fields to incorporate best practices into the project. Required Qualifications: • Education: Currently enrolled in, or recent graduate of a graduate program (Master’s or Ph.D.) in Data Science, Computer Science, Machine Learning, or a closely related field. • Technical Skills: o Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or Keras. o Strong programming skills in Python. • Analytical Skills: Strong understanding of statistical and machine learning algorithms, including supervised and unsupervised techniques. • Research Experience: Proven experience in conducting research projects, with a strong emphasis on machine learning or data science. • Problem-Solving: Strong analytical and problem-solving skills with the ability to work independently and in a team environment.

Preferred Qualifications: • Experience with deep learning frameworks (e.g., TensorFlow, PyTorch). • Experience with computer vision techniques and libraries such as OpenCV, scikit-image, or similar. • Publications or significant coursework in machine learning, computer vision, or related areas. • Familiarity with data visualization tools and techniques. Application Process: Interested candidates should submit the following documents: 1. Resume/CV: Detailing relevant educational background and research experience. 2. Statement: Explaining your interest in the position and highlighting any specific expertise that aligns with the job description. Max. 250 words. 3. References: Contact information for at least one academic or professional reference. Please send your application materials to [email protected] by September 15th 2024.

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What are the Top Applications of AI for Manufacturing?

The manufacturing industry is one of today’s hottest markets for AI applications . With the rise of IoT and telemetry technology, the manufacturing industry has seen an influx of data. 

Manufacturing companies are positioned at a critical juncture.

The industry is witnessing an increased demand for infrastructure to improve the collection, management, and action of massive amounts of data. Leveraging design thinking principles and MLOps , manufacturing companies can develop predictive and generative applications tailored to their businesses. 

In this post, we aim to shed some light on the applications of AI within manufacturing. We’ll explore some of the most frequent applications for AI in the manufacturing space, highlighting their benefits and providing real-world examples to illustrate their impact.

What Are the Top Applications of AI for Manufacturing?

At phData, we have worked with several leading manufacturing companies to help them better harness AI to make more informed decisions. Based on our experience, manufacturing customers have the most success (in terms of adopting AI) with the following applications:

Demand Forecasting

Whether building better demand forecasts , optimizing logistics, scheduling production, or improving inventory management, ML-driven demand forecasting delivers real predictive value. These solutions use data clustering, historical data, and present-derived features to create a multivariate time-series forecasting framework.

Demand forecasting enhances forecast accuracy by enriching features for both legacy and new products, improving inventory control, reducing costs, avoiding stockouts, and boosting profitability. Additionally, it optimizes logistics by enabling more efficient transportation planning, leading to cost savings across the manufacturing industry.

Check out this dashboard example built in Tableau that provides users with the ability to track key shipment and delivery metrics over time:

Client Example

A startup food manufacturer was utilizing social media data to track trends and find niche markets to develop new products. The marketing team was spending weeks analyzing spreadsheets of TikTok and Twitter data. 

phData implemented an end-to-end trend scoring methodology using Natural Language Processing and forecasting techniques that involved human-in-the-loop feedback. After eight short weeks of work, analysis time was reduced to less than two hours. The organization now has the ability to quickly release two new product lines to capitalize on growing food trends.

Six months after the solution was deployed, the company referred to the product as the “backbone of their business.”

97.5% decrease in time required by a human to prepare data for analysis

6 new SKUs launched using a data-driven approach

> 40,000 food-related social media hashtags analyzed with each pipeline run

Supply Risk Forecasting

Anticipating demand is the cornerstone of the supply chain. Disruption in demand has upstream and downstream implications, like shifts in procurement to canceled transportation runs. Risk forecasting solutions help businesses understand risk in their supply chain and proactively create risk-mitigating processes.

Minimizing downtime is the key to profitability.

Risk forecasting and reduction in plant disruption allow organizations to mitigate risks and maximize profits. Risk forecasting models can incorporate external data sources, such as weather and location data, to enable organizations to quickly adapt and respond to events.

A massive supply chain risk intelligence company was looking to develop an AI strategy that would enable them to better utilize the latest and greatest in ML technology to streamline and expand their business. By including location data, the business developed a strategy that notified retailers of imminent supplier disruptions. 

If a disruption is detected, the model directs the retailer toward a supplier in a different region, decreasing downtime and maintaining throughput.

Predictive Maintenance

By leveraging IoT , telemetry, and historical maintenance data, predictive maintenance models help businesses determine the optimal time to service equipment and decrease downtime.

Organizations can maximize profitability through predictive maintenance and tighter downtime management.

Predictive maintenance provides insights for line and shift leaders to better manage downtime, quality issues, and productivity goals. Predictive measures benefit plant leadership, enhancing quality control visibility. Some individuals may be innately reactive to problems. As a result, predictive maintenance solutions can reposition line and shift leaders toward more proactive mindsets.

A global food manufacturer needed to lessen downtime, reduce operating costs, create more visibility in their systems, and shift from a reactive to a proactive mindset. The organization partnered with phData to create a standard time series data model of demand, quality, productivity, and safety data, allowing end users to view key metrics in one source of truth location.

Visualizations were built on top of the data model to meet the needs of plant leadership and operational staff around cost reduction, visibility, and a proactive mindset.

“Since our engagement with phData, our NPS scores have improved quarter over quarter.”

80% forecast accuracy

Quality Assurance

In manufacturing, fidelity is key. Incomplete or faulty connections and even minor blemishes all impact the end result. Consistency and accuracy in identifying defects are crucial to a successful assembly.

An emerging technology in the computer vision space, LandingAI , tackles these challenges particularly well.

“LandingAI’s LandingLens™ provides an AI/Deep Learning visual inspection development and deployment platform that helps OEMs, system integrators, and distributors to easily evaluate AI/Deep Learning model efficacy for a single application or as part of a hybrid solution combined with traditional 2D/3D machine vision and robotic control solutions.”

LandingAI’s computer vision solution is exceptionally useful in the manufacturing space. The insights gleaned from LandingAI’s proprietary images can aid in:

Assembly Inspection

Quality Control Inspection

Compliance and Regulatory Measures

Electronics Manufacturing Inspection

Textile Quality Control

Equipment Monitoring

To learn more about this powerful technology, check out this blog that shows how to train and deploy an object detection model with 100% accuracy using LandingAI.

A construction and engineering organization was interested in developing insights into construction sites where accidents go unnoticed and alerts when an individual is in a certain area or at a particular time of day.

The organization was able to utilize LandingAI’s computer vision models to implement personnel monitoring, detection, and alerting measures at construction sites.

In Conclusion

Manufacturing is benefiting massively from the present AI boom. From predictive maintenance to identifying parts defects and novel product avenues, AI applications are relevant across all aspects of the manufacturing industry.

If any of the AI applications covered in this blog interest you, phData can help your business implement them.

Leading manufacturing companies often leverage phData’s AI expertise to executed AI solutions that strengthen manufacturing plant safety, efficiency, and productivity, driving business growth and increasing profitability. Contact us today to learn more!

Migrations from legacy on-prem systems to cloud data platforms, like Snowflake and Redshift

Creation of dashboards and reports for descriptive analytics, control-tower supply chain reporting, and forecast visibility

Development of AI/ML predictive applications for forecasting and predictive maintenance

To remain competitive, manufacturing companies need to leverage data spanning a wide variety of sources. Centralizing and harmonizing that data can be a significant challenge. Once data has been centralized, building reports and analytical applications require a mix of both deep expertise and innovative creativity.

The modern economy runs on data, and manufacturing organizations have always relied on data within their business. New technologies have opened the doors for groundbreaking transformations, including: 

Using data to improve visibility across the entire business or supply chain

Leveraging next-generation AI/ML technology to improve forecasting or marketing

Harmonizing data across various IoT/telemetry sources to understand performance

More to explore

How to Pick the Right Use Case for AI

Building AI with Data-Centric Test Development

Experimenting with GenAI: Building Self-Healing CI/CD Pipelines for dbt Cloud

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Purdue’s online data science master’s addresses burgeoning demand for trained data scientists

The interdisciplinary degree is accessible for working professionals from both technical and nontechnical backgrounds

WEST LAFAYETTE, Ind. — Data scientists who can make sense of today’s epic floods of data to generate actionable insights and communicate them to a variety of audiences are in demand in almost any field, from retail business and industry to health care, government, education, and more.

The U.S. Bureau of Labor Statistics estimates that jobs for data scientists will grow 36% by 2031. Nationally, there were nearly 125,000 data scientist jobs added from 2013-2023. Yet many of those jobs — with many more openings coming — went unfilled for a lack of trained data scientists. The bottom line: Nearly every industry today requires data scientists, and the number of these positions is expected to grow.

Purdue University’s new 100% online Master of Science in data science degree addresses the need and the high demand for a trained data science workforce that can harness the power of data to drive innovation, efficiency and competitiveness. The interdisciplinary master’s program is designed for working professionals with a technical background but includes a pathway to entry for professionals from nontechnical fields.

“This data science master’s program is specifically designed for online delivery and optimal online learning, making it accessible to professionals around the world,” said Dimitrios Peroulis, Purdue senior vice president for partnerships and online. “The interdisciplinary curriculum is diverse, customizable to a student’s needs and tailored for practical application immediately.”

Purdue’s online master’s in data science features core courses covering foundations of data science, machine learning and data mining, big data technologies and tools, data analysis, and data visualization and communication.

Students do a capstone project pairing them with an industry mentor and a collaborative team to manage a data science project from inception to completion. That includes developing project timelines, allocating resources and adapting strategies based on the project’s evolution. The capstone, modeled after curriculum from The Data Mine , Purdue’s award-winning data science learning community, is an opportunity to apply knowledge acquired throughout the master’s program to solve complex, real-world problems.

The online master’s program also features the opportunity to earn industry-aligned certificates along the way to earning a master’s degree. Options include education, leadership, and policy; smart mobility and smart transportation; data science in finance; spatial data science; geospatial information science; managing information technology projects; IT business analysis; and applied statistics.

The program was developed by an interdisciplinary cohort of expert faculty from Purdue’s flagship campus, including the colleges of Agriculture, Education, Engineering, Health and Human Sciences, Liberal Arts, Pharmacy, Science, and Veterinary Medicine, along with the Mitch Daniels School of Business, the Purdue Polytechnic Institute, the Purdue Libraries, and the Office of the Vice Provost for Graduate Students and Postdoctoral Scholars.

“Purdue’s new online MS in data science program leverages the real-world experience of faculty working across several distinct disciplines,” said Timothy Keaton, assistant professor of practice in Purdue’s Department of Statistics, who was involved in developing the new degree. “This cooperation between experts in the application of data science in diverse fields provides a great opportunity to create engaging and meaningful coursework that incorporates many different potential areas of interest for our students.”

Students will develop expertise in programming languages, gaining the ability to design and implement data-driven solutions; learn to apply advanced technologies, including cloud computing and big data frameworks, to effectively handle and process large-scale datasets; gain a deep understanding of machine learning algorithms and models, applying them to real-world scenarios; and become proficient in collecting, cleaning, and analyzing diverse datasets.

The curriculum also is designed to teach learners data visualization and communication methods for creating compelling visual representations of complex data to effectively convey insights, along with the application of storytelling techniques to communicate findings clearly to both technical and nontechnical audiences. The program covers adherence to ethical standards in data science, privacy, transparency and fairness as well.

The program draws on Purdue’s expertise in myriad aspects of data science. Known for its emphasis on practical programs with proven value, Purdue has been rated among the Top 10 Most Innovative Schools for six years running by U.S. News & World Report and is the No. 8 public university in the U.S. according to the latest QS World University Rankings.

“The breadth and depth of topics that data science encompasses necessitate graduate programs that incorporate expertise from a variety of disciplines and then integrate this into a curriculum to meet the needs of its students,” said John Springer, a Purdue computer and information technology professor who was involved in developing the new degree. “Purdue’s unique approach to the development and delivery of its new online master’s program wholly fulfills these requirements by utilizing a highly interdisciplinary team of Purdue faculty backed by Purdue’s outstanding team of instructional designers.”

For more information about Purdue’s 100% online Master of Science in data science degree, visit the program website .

About Purdue University

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the Mitch Daniels School of Business, Purdue Computes and the One Health initiative — at https://www.purdue.edu/president/strategic-initiatives .

Media contact: Brian Huchel, [email protected]

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August 26, 2024

Today’s top 5 from Purdue University

August 23, 2024

From Linguistics to Multi-modal Machine Learning: Learning is Our Best Capacity!

Ever wondered how language is acquired in early childhood? Want to explore the multi-modal machine learning application for language acquisition? Curious about the strategies that could help expand our learning capacities for all ages? Our guest, Alvin Tan, a current PhD student at the Stanford Language and Cognition Lab shares his unique journey and how he has continued to pursue his curiosities amidst life's twists and turns. 

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Benchmarking Machine Learning Algorithms to Predict Profitability Directional Changes

• Conference paper
• First Online: 28 August 2024
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• Panagiotis G. Artikis 23 ,
• Nicholas D. Belesis 23 ,
• Georgios A. Papanastasopoulos 23 &
• Antonios M. Vasilatos 23  

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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This study evaluates machine learning techniques, including Random Forest, Stochastic Gradient Boosting, and AdaBoost, against Logistic Regression in predicting European profitability directional changes. The research addresses the growing need for better prediction models in financial analysis. Focusing on the superiority of machine learning, the study investigates cross-validation strategies, finding that classic methods outperform rolling forward. Results reveal constant high accuracy across predicting horizons, challenging conventional methods. DuPont analysis and raw accounting data are employed, with raw data being as insightful as financial ratios. The research contributes methodologically by demonstrating the robustness of machine learning and pushing for practical computational efficiency. Implications extend beyond academics and industry, directing the design of prediction models and underlining the necessity of different data sources. Future research could explore machine learning for metrics of profitability in levels and assess the value relevance of raw accounting items. This research aligns with literature while providing fresh insights into predictive modeling in financial analysis.

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Anand, V., Brunner, R., Ikegwu, K., & Sougiannis, T. (2019). Predicting profitability using machine learning. Available at SSRN 3466478.

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Acknowledgements

This work has been partly supported by the University of Piraeus Research Center.

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Panagiotis G. Artikis, Nicholas D. Belesis, Georgios A. Papanastasopoulos & Antonios M. Vasilatos

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College of Business Administration, University of Sharjah, Sharjah, United Arab Emirates

Panagiotis D. Zervopoulos

University of Sharjah, College of Business Administration, University of Sharjah (UAE) and Nisantasi University (Turkey), Sharjah, United Arab Emirates

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Artikis, P.G., Belesis, N.D., Papanastasopoulos, G.A., Vasilatos, A.M. (2024). Benchmarking Machine Learning Algorithms to Predict Profitability Directional Changes. In: Emrouznejad, A., Zervopoulos, P.D., Ozturk, I., Jamali, D., Rice, J. (eds) Business Analytics and Decision Making in Practice. ICBAP 2024. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-61589-4_8

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Lindner announces Quantitative Finance graduate certificate

Students can learn in-person or online, receive experiential learning opportunities.

The Carl H. Lindner College of Business has launched the Quantitative Finance graduate certificate, which provides relevant financial context to modern quantitative tools in the merging spheres of finance and technology, or "fintech," such econometric analysis, machine learning, programming, blockchain and large language models.

Lindner graduate certificates allow students to gain specialized business knowledge to enhance their career options. Quantitative Finance can be completed in one to two semesters of study. The certificate can be taken as a standalone certificate or is easily combined with a Lindner MBA or one of the college’s other Master of Science programs, particularly Applied Economics , Business Analytics , Finance and Information Systems .

Quantitative Finance can be fulfilled through in-person and online modalities and is accepting applications for the spring 2025 semester. Prior experience or academic training in finance or analytics is helpful, but not required, for this certificate.

We are very much trying to stay at the forefront of the market, so students can elevate their careers.

Michael Neugent Associate professor-educator of finance

“The quantitative finance certificate adds financial context to quantitative tools possessed by applicants coming into the program with a technical/analytical background and provides quantitative tools to those coming in with a background in finance or related fields,” said Michael Ferguson , PhD, associate professor and head of the department of finance, real estate, and insurance and risk management. “Classes will have students with a range of technical and financial backgrounds, so you will learn from your classmates and the faculty.”

Quantitative Finance provides students with flexibility (in-person or online options); hands-on experiential learning opportunities; access to expert faculty who are leading scholars in their area of expertise and have extensive private industry experience; and a connection to Lindner's 50,000-strong alumni network , who can aid certificate recipients in landing job placements in an expanding talent field.

The curriculum features a compulsory two-credit-hour course in quantitative equity investing. Students must also complete two separate three-credit-hour courses, with two options for each course slot. Finally, students must fulfill four elective credit hours, with a trio of two-credit-hour courses to select from.

This certificate matches up with a range of job opportunities and career fields, from loan officers and financial, operations and portfolio managers, to budget, credit, equity, and financial and investment analysts.

“We are very much trying to stay at the forefront of the market, so students can elevate their careers,” said Michael Neugent , associate professor-educator of finance and director of the MS Finance program. “Students come to us because they either want to grow within their own company or move to other positions. We have to prepare them for that, and the certificate gets them into that mindset.”

The graduate certificate application  priority deadline for the spring 2025 semester is Oct. 15, with the final deadline on Dec. 1.

Featured image at top courtesy of Adobe Stock.

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Lindner graduate certificate programs are fast (complete a certificate in as little as eight months), focused (specialized knowledge of a particular business function) and relevant (provide students with new skills and teach you how to leverage your existing expertise). Apply today .

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Report: Rent has increased 175% faster than household income over past 20 years

March 25, 2021

Mike Eriksen, PhD, West Shell Associate Professor of Real Estate from the Carl H. Lindner College of Business, recently published a report entitled, “The Location of Affordable and Subsidized Rental Housing Across and Within the Largest Cities in the United States” with the Mortgage Bankers Association’s Research Institute for Housing America.

Announcing the General Availability of the VS Code extension for Azure Machine Learning

August 22nd, 2024 0 0

Machine learning and artificial intelligence are transforming the world as we know it. With the power of data, you will have countless opportunities to create something new, unique, and exciting. Whether you are a seasoned data scientist or a curious beginner, you need a platform that can help you build, train, deploy, and manage your machine learning models with ease and efficiency. Azure Machine Learning has always been the backbone for machine learning tasks, and we want to further help you in your machine learning journey by improving the way you write code.

The VS Code extension for Azure Machine Learning has been in preview for a while and we are excited to announce the general availability of the VS Code extension for Azure Machine Learning. You can use your favorite VS Code setup, either desktop or web, to build, train, deploy, debug, and manage machine learning models with Azure Machine Learning from within VS Code. This means that the extension is stable, reliable, ready for production use, and comes with additional features, such as VNET support.

“We have been using the VS Code extension for Azure Machine Learning since its preview release, and it has significantly streamlined our workflow. The ability to manage everything from building to deploying models directly within our preferred VS Code environment has been a game-changer. The seamless integration and robust features like interactive debugging and VNET support have enhanced our productivity and collaboration. We are thrilled about its general availability and look forward to leveraging its full potential in our AI projects.” – Ornaldo Ribas Fernandes: Co-founder and CEO, Fashable

Azure Machine Learning

Azure Machine Learning (Azure ML) is a cloud-based service that enables you to build, train, deploy, and manage machine learning models.

With Azure Machine Learning service, you can:

• Build and train machine learning models faster, and easily deploy to the cloud or the edge.
• Use the latest open-source technologies such as TensorFlow, PyTorch, or Jupyter.
• Experiment locally and then quickly scale up or out with large GPU-enabled clusters in the cloud.
• Interactively debug experiments, pipelines, and deployments using the built-in VS Code debugger.
• Speed up data science with automated machine learning and hyper-parameter tuning.
• Track your experiments, manage models, and easily deploy with integrated CI/CD tooling.

With this extension installed, you can accomplish much of this workflow directly from Visual Studio Code. The VS Code extension provides a user interface to create and manage Azure ML resources, such as experiments, compute targets, environments, and deployments. It also supports the Azure ML 2.0 CLI, which is the new command-line tool that simplifies the specification and execution of machine learning tasks.

Get Started with Azure Machine Learning Extension

One click connect to vs code from azure ml studio.

To get started with VS Code, navigate to the compute section of your Azure Machine Learning Studio . Find the desired compute instance and click on the VS Code (Web) or VS Code (Desktop) links under the “Applications” section.

Don’t have an Azure ML workspace or compute instance? Check out the guide here: Tutorial: Create workspace resources – Azure Machine Learning | Microsoft Learn

VS Code Desktop

After clicking on the link for VS Code desktop, the browser will ask you for your permission to launch the VS Code Desktop application. VS Code desktop will ask you to sign in using your Microsoft/Azure account.

Follow the sign-in prompts, then you should be all set up to develop your own machine learning models using your favorite VS Code set up!

VS Code Web

After clicking on the link, VS Code (Web) will open to a new tab on your browser. It may ask you to sign in using your Microsoft/Azure account, so VS Code will have permission to access your Azure subscription and workspace. Note the connection process may take a few minutes.

After signing in, you should now be connected to your Azure Machine Learning workspace inside of VS Code. Time to build your own machine learning model using the full power of VS Code!

Give the Azure Machine Learning extension a try and let us know what you think. If you have any questions or feedback, please let us know your thoughts in this survey ! You can also file an issue on our public GitHub repo with any questions or concerns you may have.

Need a guide to help you get started or documentation? Check out the tutorials here: Azure Machine Learning documentation | Microsoft Learn

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While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. This post will dive deeper into the nuances of each field.

Data science is a broad, multidisciplinary field that extracts value from today’s massive data sets. It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve. Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem.

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. It requires data science tools to first clean, prepare and analyze unstructured big data. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed.

Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day. While it can be tedious, it’s critical to get it right.

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored.

One challenge in applying data science is to identify pertinent business issues. For example, is the problem related to declining revenue or production bottlenecks? Are you looking for a pattern you suspect is there, but that’s hard to detect? Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

With the increase in data from social media, e-commerce sites, internet searches, customer surveys and elsewhere, a new field of study based on big data emerged. Those vast datasets, which continue to increase, let organizations monitor buying patterns and behaviors and make predictions.

Because the datasets are unstructured, though, it can be complicated and time-consuming to interpret the data for decision-making. That’s where data science comes in.

The term data science (link resides outside of ibm.com) was first used in the 1960s when it was interchangeable with the phrase “computer science.” “Data science” was first used as an independent discipline  (link resides outside of ibm.com) in 2001. Both data science and machine learning are used by data engineers and in almost every industry.

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know  Structured Query Language (SQL)  as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable. as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable.

Data science is widely used in industry and government, where it helps drive profits, innovate products and services, improve infrastructure and public systems and more.

Some examples of data science use cases include:

• An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.
• A manufacturer developed powerful, 3D-printed sensors to guide driverless vehicles.
• A police department’s statistical incident analysis tool helps determine when and where to deploy officers for the most efficient crime prevention.
• An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.
• Healthcare companies are using data science for breast cancer prediction and other uses.
• One ride-hailing transportation company uses big data analytics to predict supply and demand, so they can have drivers at the most popular locations in real time. The company also uses data science in forecasting, global intelligence, mapping, pricing and other business decisions.
• An e-commerce conglomeration uses predictive analytics in its recommendation engine.
• An online hospitality company uses data science to ensure diversity in its hiring practices, improve search capabilities and determine host preferences, among other meaningful insights. The company made its data open-source, and trains and empowers employees to take advantage of data-driven insights.
• A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions.

The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test  (link resides outside of ibm.com), which asked the question, “Can machines think?” The test is whether a machine can engage in conversation without a human realizing it’s a machine. On a broader level, it asks if machines can demonstrate human intelligence. This led to the theory and development of AI.

IBM computer scientist Arthur Samuel (link resides outside of ibm.com) coined the phrase “machine learning” in 1952. He wrote a checkers-playing program that same year. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won.

Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages. Python is the most common programming language used in machine learning.

Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. It can recognize complex patterns in text, images, sounds, and other data and create accurate insights and predictions. Deep learning algorithms are neural networks modeled after the human brain.

Subcategories of machine learning

Some of the most commonly used machine learning algorithms  (link resides outside of ibm.com) include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm . These can be supervised learning, unsupervised learning or reinforced/reinforcement learning.

Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more.

There are some ethical concerns regarding machine learning, such as privacy and how data is used. Unstructured data has been gathered from social media sites without the users’ knowledge or consent. Although license agreements might specify how that data can be used, many social media users don’t read that fine print.

Another problem is that we don’t always know how machine learning algorithms work and “make decisions.” One solution to that may be releasing machine learning programs as open-source, so that people can check source code.

Some machine-learning models have used datasets with biased data, which passes through to the machine-learning outcomes. Accountability in machine learning refers to how much a person can see and correct the algorithm and who is responsible if there are problems with the outcome.

Some people worry that AI and machine learning will eliminate jobs. While it may change the types of jobs that are available, machine learning is expected to create new and different positions. In many instances, it handles routine, repetitive work, freeing humans to move on to jobs requiring more creativity and having a higher impact.

Well-known companies using machine learning include social media platforms, which gather large amounts of data and then use a person’s previous behavior to forecast and predict their interests and desires. The platforms then use that information and predictive modeling to recommend relevant products, services or articles.

On-demand video subscription companies and their recommendation engines are another example of machine learning use, as is the rapid development of self-driving cars. Other companies using machine learning are tech companies, cloud computing platforms, athletic clothing and equipment companies, electric vehicle manufacturers, space aviation companies, and many others.

Practicing data science comes with challenges. There can be fragmented data, a short supply of data science skills, and tools, practices, and frameworks to choose between that have rigid IT standards for training and deployment. It can also be challenging to operationalize ML models that have unclear accuracy and predictions that are difficult to audit.

IBM’s data science and AI lifecycle product portfolio is built upon our longstanding commitment to open-source technologies. It includes a range of capabilities that enable enterprises to unlock the value of their data in new ways.

Watsonx  is a next generation data and AI platform built to help organizations multiply the power of AI for business. The platform comprises three powerful components: the  watsonx.ai  studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.

Together, watsonx offers organizations the ability to:

• Train, tune and deploy AI across your business with  watsonx.ai
• Scale AI workloads, for all your data, anywhere with  watsonx.data
• Enable responsible, transparent and explainable data and AI workflows with  watsonx.governance

Learn more about IBM watsonx

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JSmol Viewer

A machine learning predictive model for ship fuel consumption.

Featured Application

1. introduction, 2. materials and methods, 2.1. data acquisition, 2.2. database, 2.3. data processing, 2.3.1. filling in missing data, 2.3.2. transformation of categorical variables, 2.3.3. removing the outliers, 2.3.4. standardization, 2.4. feature selection.

• The strength of the relationship between the variables could admit any value between −1 and +1, and the closer to one of the extremes, the stronger the correlation would be; it is further assumed that for a perfect linear relationship to occur, the correlation shown should be equal to −1 or +1;
• When equal to zero, there is no linear relationship between the two variables under analysis;
• A positive value of the correlation coefficient would determine the existence of a directly proportional relationship between two variables; that is, as one of them grows, the behavior of the curve of the other variable will also increase; on the other hand, for a negative coefficient, the attributes would be considered inversely proportional, which, in other words, means that as one attribute increases, the other decreases).

2.5. Model Building and Training

2.5.1. decision tree, 2.5.2. assembly method, random forest, extra trees, gradient boosting (gb), extreme gradient boosting (xgboost), 2.6. hyperparameters tuning, 2.7. model performance evaluation, 2.7.1. mean absolute error (mae), 2.7.2. mean squared error (mse), 2.7.3. root mean square error (rmse), 2.7.4. coefficient of determination (r 2 ), 2.8. model validation, 3. analysis of results, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Melo, R.F.; Figueiredo, N.M.d.; Tobias, M.S.G.; Afonso, P. A Machine Learning Predictive Model for Ship Fuel Consumption. Appl. Sci. 2024 , 14 , 7534. https://doi.org/10.3390/app14177534

Melo RF, Figueiredo NMd, Tobias MSG, Afonso P. A Machine Learning Predictive Model for Ship Fuel Consumption. Applied Sciences . 2024; 14(17):7534. https://doi.org/10.3390/app14177534

Melo, Rhuan Fracalossi, Nelio Moura de Figueiredo, Maisa Sales Gama Tobias, and Paulo Afonso. 2024. "A Machine Learning Predictive Model for Ship Fuel Consumption" Applied Sciences 14, no. 17: 7534. https://doi.org/10.3390/app14177534

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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.

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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.

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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.

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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

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

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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 - CMU

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Graduate Education

Office of graduate and postdoctoral education, machine learning (ml), 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: March 3
• 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 an admissions checklist to help you navigate through the admissions process.

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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..

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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Carnegie Mellon University School of Computer Science

Machine learning department.

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

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Georgia Institute of Technology

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PhD Program

The machine learning (ML) Ph.D. program is a collaborative venture between Georgia Tech's colleges of Computing, Engineering, and Sciences. Approximately 25-30 students enter the program each year through nine different academic units. 

ML@GT manages all operations and curricular requirements for the new Ph.D. Program, which include four core and five elective courses, a qualifying exam, and a doctoral dissertation defense .

See the curriculum overview for more information.

Students admitted into the ML Ph.D. program can be advised by any of our  participating ML Ph.D. Program faculty .

More information about admission to the ML Ph.D. program can be found here .

More information about the program itself, including details on operations and curriculum outlined in the ML Handbook, can be found in the current student resources.

ML@GT Ph.D. Faculty Advisory Committee

• Aerospace Engineering (AE): Evangelos Theodorou, [email protected]
• Biomedical Engineering (BME): May Wang, [email protected]
• Chemical and Biomolecular Engineering (ChBE): Martha Grover, [email protected]
• Electrical Computer Engineering (ECE): David Anderson, [email protected]
• Industrial Systems Engineering (ISyE): Yao Xie, [email protected]
• College of Computing (IC, SCS, CSE): Zsolt Kira, [email protected]
• Mathematics (MATH): Vladimir Koltchinskii, [email protected]

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Georgia Institute of Technology North Avenue, Atlanta, GA 30332 Phone: 404-894-2000

Best Universities for Machine Learning in the World

Updated: February 29, 2024

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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 165M citations received by 7.75M academic papers made by 5,307 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

2. University of California - Berkeley

3. Harvard University

4. University of Michigan - Ann Arbor

5. University of Toronto

6. University of Washington - Seattle

7. Carnegie Mellon University

8. Massachusetts Institute of Technology

9. Tsinghua University

10. University of Illinois at Urbana - Champaign

11. University of Oxford

12. University of California - Los Angeles

13. Cornell University

14. University College London

15. University of Minnesota - Twin Cities

16. Johns Hopkins University

17. Nanyang Technological University

18. University of California-San Diego

19. University of Wisconsin - Madison

20. National University of Singapore

21. Pennsylvania State University

22. University of Pennsylvania

23. University of Cambridge

24. Columbia University

25. Shanghai Jiao Tong University

26. University of Southern California

27. New York University

28. University of Texas at Austin

29. University of Hong Kong

30. Yale University

31. Georgia Institute of Technology

32. University of Maryland - College Park

33. Ohio State University

34. Imperial College London

35. Catholic University of Leuven

36. Princeton University

37. University of North Carolina at Chapel Hill

38. University of British Columbia

39. University of Chicago

40. Harbin Institute of Technology

41. Peking University

42. Arizona State University - Tempe

43. Huazhong University of Science and Technology

44. Michigan State University

45. University of Sydney

46. Zhejiang University

47. Duke University

48. Swiss Federal Institute of Technology Zurich

49. Texas A&M University - College Station

50. Technical University of Munich

51. University of Florida

52. Chinese University of Hong Kong

53. University of Melbourne

54. University of Alberta

55. University of New South Wales

56. University of Amsterdam

57. University of Tokyo

58. Federal Institute of Technology Lausanne

59. University of Edinburgh

60. Rutgers University - New Brunswick

61. University of Pittsburgh

62. Xi'an Jiaotong University

63. Purdue University

64. Beihang University

65. University of Waterloo

66. University of Electronic Science and Technology of China

67. Boston University

68. Hong Kong Polytechnic University

69. University of California - Davis

70. University of Manchester

71. McGill University

72. Wuhan University

73. Northwestern University

74. National Taiwan University

75. University of California - Irvine

76. Southeast University

77. University of Montreal

78. Central South University

79. University of Queensland

80. Delft University of Technology

81. Seoul National University

82. Iowa State University

83. Sun Yat - Sen University

84. University of Arizona

85. Monash University

86. University of California - San Francisco

87. University of Science and Technology of China

88. Northwestern Polytechnical University

89. Virginia Polytechnic Institute and State University

90. City University of Hong Kong

91. University of Sao Paulo

92. University of Massachusetts - Amherst

93. University of Bristol

94. University of Sheffield

95. Australian National University

96. California Institute of Technology

97. Hong Kong University of Science and Technology

98. Dalian University of Technology

99. North Carolina State University at Raleigh

100. Xidian University

Computer Science subfields in the World