Detailed Information

Course Form

This course is given is a one week course in August 2013 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 CampusNet or by enquiery to Christina Horn Nexoe chne[a]imm.dtu.dk. For information on how to apply via Open University, see this link.

Location

All lectures will be given at the DTU main campus, building 308, room 017. Participants will be offered lunch each day, and there will be coffee and refreshments available.

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 followed by exercises are in modules of half a day for each subject (8-12 o'clock and 13-17 o'clock). We will make arrangements for lunch from 12-13. The schedule is from last year - the content will be the same - but we may shuffle around with lecturers and modules.

Module Date Subjects Lecturer Litterature
1 19/8 Introduction to computational data analysis. Linear regression and classification LAAR ESL Chapters 3.1, 3.2, 3.4.1, 4.1, and 4.3
2 29/8 Model selection LHC ESL Chapter 7. You may safely skip sections 7.8 and 7.9
3 20/8 Logistic regression,
optimal separating hyperplanes,
basis expansions and
support vector machines		 LAAR ESL Chapters 4.4, 4.5, 5.1, 5.2, 12.1, 12.2, 12.3.1
4 20/8 Cluster analysis LHC ESL Chapters 14.3
5 21/8 Sparse regression and classification LHC ESL Chapters 3.3, 3.4, 18
6 21/8 Principal component analysis,
Sparse principal component analysis		 LAAR ESL Chapters 14.5.1, 14.5.5
7 22/8 Sparse coding, NMF, Archetypical Analysis and ICA MM ESL Chapters 14.6 - 14.10, [Sparse Coding, Nature]
8 22/8 Classification and Regression Trees (CART) LAAR ESL Chapter 9.2
9 23/8 Bagging, Boosting,
Random forests		 LHC ESL Chapter 15
10 23/8 Multiway models MM WireOverview.pdf available from Campusnet

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 October 1st, 2012.

Lecturers

LHC: Line H. Clemmensen, Assistant Professor, DTU Data Analysis, lhc[at]imm.dtu.dk
LAAR: Lars Arvastson, External Lecturer, DTU Data Analysis, Lundbeck, larv[at]lundbeck.com
MM: Morten Mørup, Assistant Professor, DTU Informatics, Cognitive Systems, mm[at]imm.dtu.dk

Other relevant PhD courses in the summer 2012

Advanced Topics in Machine Learning (August 13-17)