Detailed Information
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
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.
Lectures and exercises are in modules of half a day for each subject (812 o'clock and 1317 o'clock), and will take place in Building **, Room **. We will make arrangements for lunch from 1213, but students will need to pay their own lunch. The schedule is from last year  content will be: crossvalidation, model selection, biasvariance tradeoff, 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, nonnegative matrix factorization, independent component analysis, archetypical analysis, and sparse coding.
Module  Date  Subjects  Lecturer  Litterature 
1  17/8  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  17/8  Model selection  LHC  ESL Chapter 7. You may safely skip sections 7.8 and 7.9 
3  18/8  Classification methods  LAAR  ESL Chapters 4.4, 4.5, 5.1, 5.2, 12.1, 12.2, 12.3.1 
4  18/8  Sparse regression and classification  LHC  ESL Chapters 3.3, 3.4, 18 
5  19/8  Cluster analysis  LAAR  ESL Chapters 14.3 
6  19/8  Principal component analysis, Sparse principal component analysis 
LHC  ESL Chapters 14.5.1, 14.5.5 
7  20/8  Unsupervised decompositions  ESL Chapters 14.6  14.10, [Sparse Coding, Nature]


8  20/8  Tree Based Methods  LAAR  ESL Chapter 9.2 
9  21/8  Neural Networks  WireOverview.pdf available from Campusnet  
10  21/8  Ensemble methods  LHC  ESL Chapter 15 
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/nonpassed. Deadline for the report is September 25th, 2018.
LHC: Line H. Clemmensen, Associate Professor, DTU Compute, Statistics and Data Analysis, lkhc[at]dtu.dk
LARV: Lars Arvastson, External Lecturer, Lundbeck,
larv[at]lundbeck.com