Machine Learning, Graduate Course

Spring 2011

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.

Contents

• Linear regression
• Linear classification
• Neural networks
• Support vector machines
• Expectation Maximization (EM)
• Clustering
• Approximate inference (VB and EP)
• Graphical models
• Boosting
• Sampling methods and MCMC

Periodicity

Every 2 years.

Organization and Examination

The course gives 9 hp (you can receive an additional 3 hp by carrying out a project).

• Lectures: 12

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

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 3 day (72 h) exam. The exam period is April 5 - May 5, 2011.

Contact Person

Dr Thomas Schön, tel 013 - 281373, email: schon_at_isy.liu.se.