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

Seminars

Project

Self-study literature review

Written assignments

Seminars given by participants

Compulsory assignments including computer work and written reports. Assignments shall be peer-reviwed and be discussed in the study group.

Failed, pass

FMA420 Linear Algebra, FMA430 Calculus in Several Variables, Fourier analysis and theory of linear systems corresponding to FMAF05 Mathematics-Systems and Transforms, and one of the basic courses in Mathematical Statistics, e.g. FMS012.

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

Examiner: Maria Kihl (maria.kihl@eit.lth.se)

EIT155F

 -12-08

Professor Thomas Johansson