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.
- Linear regression
- Linear classification
- Neural networks
- Support vector machines
- Gaussian processes
- Expectation Maximization (EM)
- Approximate inference (VB and EP)
- Introducing graphical models
- 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
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.
PrerequisitesBasic undergraduate courses in linear algebra, statistics, signal and systems.
Related CoursesSystem identification, sensor fusion.
ExamStandard 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.
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.
Associate Professor in Automatic Control
- +46 13 281373
- Mobile (private):
- +46 735 933 887
- Dept. of Electrical Engineering
- Linköping University
- SE-581 83 Linköping
- Visiting Address:
- Campus Valla
- Building B
- Room 2A:NNN (in the A corridor on the ground floor between entrance 25 and 27)
Page responsible: Thomas Schön
Last updated: 2012-10-13