Detailed Information

Course Form

This course is given is a one week course in August 2018 at The Technical University of Denmark. 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

Location

All lectures will be given at the DTU main campus.

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.

Microarray example (from Wikipedia).

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 in Building **, Room **. We will make arrangements for lunch from 12-13, but students will need to pay their own lunch. The schedule is from last year - 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.

Module Date Subjects Lecturer Litterature
1 20/8 Introduction to computational data analysis [OLS, Ridge] Line/Lars ESL Chapters 1, 2, 3.1, 3.2, 3.4.1, 4.1
2 20/8 Model selection [CV, Bootstrap, Cp, AIC, BIC, ROC] Line ESL Chapter 7 and 9.2.5. You may safely skip sections 7.8 and 7.9
3 21/8 Sparse regression [Lasso, elastic net] Line ESL Chapters 3.3, 3.4, 18.1, and 18.7
4 21/8 Sparse classifiers [LDA, Logistic regression] Lars ESL Chapters 4.3, 4.4, 18.2, 18.3, 18.4, 5.1, and 5.2
5 22/8 Nonlinear learners [Support vector machines, CART and KNN] Lars ESL Chapters 4.5, 4.4, 5.1, 5.2, 9.2 and 13.3
6 22/8 Ensemble methods [Bagging, random forest, boosting] Line ESL Chapter 8.7, 9.2, 10.1 and 15
7 23/8 Subspace methods [PCA, SPCA, PLS, CCA, PCR] Line ESL Chapters 14.5.1, 14.5.5 and 3.5
8 23/8 Unsupervised decompositions [ICA, NMF, AA, Sparse Coding] Morten ESL Chapters 14.6 - 14.10, [Sparse Coding, Nature]
9 24/8 Cluster analysis [Hierarchical, K-means, GMM, Gap-Statistic] Line ESL Chapter 14.3
10 24/8 Artificial Neural Networks and Self Organizing Maps Lars 11.1-11.5 and 14.5

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 September 25th, 2018.

Lecturers

Line H. Clemmensen, Associate Professor, DTU Compute, Statistics and Data Analysis, lkhc[at]dtu.dk
Lars Arvastson, External Lecturer, Lundbeck, larv[at]lundbeck.com
Morten Morup, Associate Professor, DTU Compute, Cognitive Systems, mmor[at]dtu.dk