2-INF-150 Machine Learning

Evaluation during the course:

Concluding evaluation:

Subject aim: Introduction of basic mathematical models of learning used in field of machine learning. Formalization and analysis of various algorithmic processes by means of computational learning theory.

Brief curriculum of the subject:

Learning from examples. Concepts, learning of concepts. Inductive inference. Identification concept models: exact identification, PAC (Probably Approximately Correct) learning, active learning. Occam‘s razor. Analysis of representation schemas from learning point of view: Boolean functions in DNF, decision trees, DFA, geometric defined hypothesis, neural networks. Boosting. Application of machine learning to data mining. Inductive logic programming.

Literature:

M. Anthony, N. Biggs: Computational Learning Theory. Cambridge University Press, 1992.

M. Kearns, U. Vazirani: An Introduction to Computational Learning Theory. MIT Press, 1994.

T. Mitchell: Machine Learning. McGraw Hill, 1997.

P. Návrat a kol.: Umelá inteligencia. STU Bratislava, 2002.

Language in which the subject is taught: Slovak, English

Date of the last sheet revision: 30.6.2005