PhD course, 2013
Data is becoming more and more widely available and the world is now in a situation where there is more data than we can handle. This clearly calls for new technology and this challenge has resulted in the rapid growth of the machine learning area over the past decade. This course provides an introduction into the area of machine learning, focusing on dynamical systems. To a large extent this involves probabilistic modeling in order to be able to solve a wide range of problems.
- Linear regression
- Linear classification
- Neural networks
- Support vector machines
- Expectation Maximization (EM)
- Approximate inference (VB and EP)
- Graphical models
- Sampling methods and MCMC
- Bayesian nonparametric (BNP) models
Organization and ExaminationThe course gives 9 hp (you can receive an additional 3 hp by carrying out a project).
- Lectures: 11
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.
Recommended supplementary reading
- Kevin P. Murphy. Machine learning - a probabilistic perspective, MIT Press, 2012.
- Daphne Koller and Nir Friedman. Probabilistic Graphical Models Principles and Techniques, MIT Press, 2012.
- David Barber. Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012.
Every 2 years.
PrerequisitesBasic undergraduate courses in linear algebra, statistics, signal and systems.
Related CoursesComputational inference in dynamical systems, System identification.
Dr Thomas Schön, tel 013 - 281373, email: schon_at_isy.liu.se.