Machine Learning

An ELLIIT PhD Course at Lund University, Lund, Sweden.

---

General Information

This course gives an introduction to machine learning, with a focus toward dynamical systems. To a large extent this involves probabilistic modelling in order to be able to solve a wide range of problems.

Lecture slides are available here.

Contents

• Linear regression
• Linear classification
• Neural networks
• Support vector machines
• Gaussian processes
• Expectation Maximization (EM)
• Clustering
• Approximate inference (VB and EP)
• Introducing graphical models
• Boosting
• MCMC and sampling methods

Organization and Examination

This is an ELLIIT graduate course that is given in order to further strengthen the cooperation between Linköping and Lund.

The course gives 6 hp (you can receive an additional 3 hp by carrying out a project).

The examination consists in a written two day take home exam.

Date and Time

This is an intensive course and it will be given in two parts,

• Part 1. May 25 - May 27, 2011
• Part 2. June 7 - June 10, 2011

Course Literature

The main book used during the course is,
[B] Christopher M. Bishop Pattern Recognition and Machine Learning, Springer, 2006.

We will also make use of,
[HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman The Elements of Statistical Learning: Data Mining, Inference and Prediction, Second edition, Springer, 2009.

Prerequisites

Basic undergraduate courses in linear algebra, statistics, signal and systems.

Related Courses

System identification, sensor fusion.

Exam

Standard 2 day (48 h) exam. The exam period is week 34 - 35 (August 22 - September 4). The exam can be collected from Eva Westin at the Department of Automatic Control.

Contact Persons

Dr Thomas Schön, tel +46 13 281373, email: schon_at_isy.liu.se.
Prof. Bo Bernhardsson, tel +46 46 222 87 86, email: bob_at_control.lth.se.
Prof. Rolf Johansson., tel +46 46 222 87 91, email: Rolf.Johansson_at_control.lth.se.