PhD Course - Computational Data Analysis
Summer 24-28 August, 2020. Course number 02910. If need be the course will run online this year!
Course Outline
New and challanging sources of data such as gene microarrays and hyperspectral images have spawned a tremendous progress in statistical modelling over the last decades. The aim of this course is to give a theoretical and applied introduction to several methods which make it possible to analyze and understand such data. These methods are also applicable to more traditional data sets, where the resulting models may perform better and/or be easier to interpret.
This course is intended for all PhD students at DTU. We will keep a focus on hands on application of the methods and limit the theory to a necessary to use the methods properly.
Because of the wide span of contents in this course, the study pace will be relatively high. However, there are plenty of room for excerises and discussion such that students will be able to complete the course despite differences in previous experience.
Exercises will primarily be based on running and altering Matlab, Python or R programs (choose the programming language you prefer). Previous experience with programming is a prerequisite. Please install Matlab, R or Python on your computer prior to course start. DTU offers a free Matlab student license.
Detailed Information
- Course Form
This course is given is a one week course in August at The Technical University of Denmark. Subsequently the students spend one month applying the methods to own data. The course is a 5 ECTS course. It is open both for all PhD students and for everyone else via Open University. DTU students should sign up using campus net. For information on how to apply via Open University, see this link . For guest PhD Students information on how to sign up is found here: Guest PhDs
All lectures will be given at the DTU main campus. If need be it will run online in 2020.
- Course Material
The course material consists of chapters from electronic textbooks and electronic papers. Most lectures will refer to the book "Elements of Statistical Learning" (ESL) by Hastie, Tibshirani and Friedman. This book is freely available from this link . References to other material will be given on CampusNet.
Schedule for the Lectures
Lectures and exercises are in modules of half a day for each subject (8-12 o'clock and 13-17 o'clock), and will take place at DTU, Lyngby Campus. We will make arrangements for lunch from 12-13, but students will need to pay their own lunch. The schedule below is subject to smaller changes - content will be: cross-validation, model selection, bias-variance trade-off, over and under fitting, sparse regression, sparse classification, logistic regression, linear discriminant analysis, clustering, classification and regression trees, multiple hypothesis testing, principal component analysis, sparse principal component analysis, support vector machines, neural netwroks, self organizing maps, random forests, boosting, non-negative matrix factorization, independent component analysis, archetypical analysis, and sparse coding.
- Examination
The student should participate in the course and hand in a small report on one or more of the course subjects related to the students' own research. The grades will be passed/non-passed. Deadline for the report is one month from the last lecture (i.e. end September).
Course responsible
Line H. Clemmensen, Associate Professor, DTU Compute, Statistics and Data Analysis, lkhc[at]dtu.dk
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