Probabilistic Models

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.

Exam

07.03.2016 16.00 B123
Year Semester Date Period Language In charge
2016 spring 19.01-03.03. 3-3 English Antti Hyttinen

Lectures

Time Room Lecturer Date
Tue 16-18 B222 Antti Hyttinen 19.01.2016-03.03.2016
Thu 16-18 B222 Antti Hyttinen 19.01.2016-03.03.2016

Exercise groups

Group: 1
Time Room Instructor Date Observe
Wed 16-18 B222 Janne Leppä-aho 25.01.2016—04.03.2016

General

All material will be available in Moodle: https://moodle.helsinki.fi/course/view.php?id=18640.