This course is one of the courses in the "Three concepts" series, and provides an introduction to issues in probability theory from a computer scientist's perspective. Many of the research issues in Artificial Intelligence, Computational Intelligence and Data Mining can be actually viewed as topics in the "science of uncertainty," which addresses the problem of optimal processing of incomplete information, i.e., plausible inference. The first part of the course introduces the use of probability for plausible inference. After a brief introduction to frequentist inference, 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. The theoretical framework will be applied for simple probabilistic modeling example tasks taken from examples in machine learning or data mining. The second part of the course is an introduction to graphical models (in particular Bayesian networks) used in such modeling tasks, and also introduces some of the software available for probabilistic modeling. This course is aimed at senior undergraduates.
The slides and the course web pages are in English, but the lectures will be given in Finnish. The posters (project IV) have to be prepared in English, and the languages used in the poster session are both Finnish and English, depending on the current visitors at your poster stand (the poster session will have guests outside the course). All written reports (project reports, poster report, and the term paper) can be either in Finnish or in English.
If there are Non-Finnish speaking students who would like to try to pass the course without attending the lectures, please contact the course instructor.
Anyone interested in such topics as intelligent agents, learning, neural networks, Bayesian inference, data mining or artificial intelligence. This course is intended to give you new ideas, approaches or tools for your problems. The course is aimed at senior undergraduates and graduate students and is required for students pursuing in M.Sc. or Ph.D. in the field of Adaptive and Intelligent Systems.
Note that the number of participants may have to be restricted. See here for more information.
The course is an introductory course, and only elementary knowledge on probability theory is required. Different parts of the course, however, have different requirements with respect to the mathematical machinery needed to apply the concepts in question. Typically some analysis and elementary mathematical statistics is required. The course projects involve programming, thus moderate programming skills are necessary.
The course does not have regular weekly assignments, but beware - this does not mean that there is no work involved! In addition to participating to the classes, the students are expected to finish five (5!) projects. When planning your schedule for the Spring semester, please note that although the lectures are given in the first period, most of the work load is concentrated in the March-May period when the projects will take place.
The amount of work required depends very much on the background of the students. With no previous knowledge of probability theory and little experience in programming, the load can be heavy. However, please note that Projects II and III can be performed in groups (size of these groups depends on the number of participants).
Participation to the classes is not enforced, but strongly encouraged, as many of the preliminaries for the projects will be discussed during the classes.
During the first period of the course (before the projects start), anybody can drop out by just sending email to the instructor. After this introductory period the project work starts, poster topics will be assigned etc. and the students are expected to be committed to the course.
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| Three Concepts: Probability | |