English

If sufficient demand

To give knowledge about the basic theory for Machine Learning -- construction of automatised systems that can learn/gather information from data, for example learn to recognize characters in a hand-written text.

* Training, testing, generalization, hypothesis spaces.
*Linear regression and classification.
*Kernel methods and support vector machines.
*Graphical models.
*Mixture models, Expectation Maximization.
*Variational and sampling methods.

* have a good knowledge of the statistical principles used in machine learning
* have knowledge of the disciplinary foundation for the design and analysis of learning algorithms and systems
* demonstrate in-depth knowledge of methods and theories in the field of machine learning.

* demonstrate abilities to develop learning techniques and systems based on relevant technological issues
* demonstrate the ability to identify, formulate, design, and implement learning components and applications.

* demonstrate the ability to critically evaluate and compare different learning models and learning algorithms for different problem setups and quality characteristics.

Lectures

Written exam

Written assignments

Take-home exam

Failed, pass

Linear algebra, calculus in several variables, linear systems, and probability theory.

Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006. ISBN 9780387310732.

The course is given by professor Cristian Sminchisescu.

FMA085F

 -03-24

FN1/Anders Gustafsson