This course is given is a one week course in August 2016 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 followed by 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. The schedule is from last year - the content will be the same - but we may shuffle around with lecturers and modules.
|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|
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||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||Sparse coding, NMF, Archetypical Analysis and ICA||MM||ESL Chapters 14.6 - 14.10, [Sparse Coding, Nature]|
|8||20/8||Tree Based Methods||LAAR||ESL Chapter 9.2|
|9||21/8||Multiway models||MM||WireOverview.pdf available from Campusnet|
|10||21/8||Bagging, Boosting, Random forests||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/non-passed. Deadline for the report is September 25th, 2015.
LHC: Line H. Clemmensen, Assistant Professor, DTU Compute, Statistics and Data Analysis, lkhc[at]dtu.dk
HERO: Helle Rootzen, Professor, DTU Compute, Statistics and Data Analysis, hero[at]dtu.dk
MM: Morten Mørup, Assistant Professor, DTU Compute, Cognitive Systems, mmor[at]dtu.dk
Advanced Topics in Machine Learning