English

Every spring semester

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, and write a report on this.

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

Lectures

Miscellaneous

Scheduled resource time during which the participants can get assistance with the assignments.

Written assignments

Failed, pass

FMAF05 Mathematics - Systems and transforms and FMS012/FMSF45 Mathematical statistics, basic course.

Credits awarded in the courses FMS012, FMSF20, FMSF10, FMSN40, FMA051, FMA120 and FMAN20.

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

FMAN45F

 -02-09

Professor Thomas Johansson