Todennäköisyysmallit

582636
5
Algorithms and machine learning
Advanced studies
This course provides an introduction to probabilistic modeling from a computer scientist"s perspective. Many of the research issues in Artificial Intelligence, Computational Intelligence and Machine Learning/Data Mining can be viewed as topics in the "science of uncertainty," which addresses the problem of optimal processing of incomplete information, i.e., plausible inference, and this course shows how the probabilistic modeling framework forms a theoretically elegant and practically useful solution to this problem. The course focuses on the "degree-of-belief" interpretation of probability and illustrates the use of Bayes" Theorem as a general rule of belief-updating. As a concrete example of methodological tools based on this approach, we will study probabilistic graphical models focusing in particular on (discrete) Bayesian networks, and on their applications in different probabilistic modeling tasks.
Year Semester Date Period Language In charge
2009 spring 13.01-19.02. Finnish

Lectures

Time Room Lecturer Date
Tue 16-18 B222 Petri Myllymäki 13.01.2009-19.02.2009
Thu 16-18 B222 Petri Myllymäki 13.01.2009-19.02.2009