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Lecture “Bayesian Learning”

General Information

Lecture

Thursdays 14-16 (22.10, 29.10, 5.11.2015, 7.1, 14.1, 21.1.2016)

Room

MAR 4.062

Teachers

Shinichi Nakajima

Contact

nakajima@tu-berlin.de

Language

English

Credits

3 ECTS, Elective Course in Machine Learning Module I (computer science M.Sc.)

Topics

Bayesian learning is a category of machine learning methods, which are based on a basic law of probability, called Bayes’ theorem. As advantages, Bayesian learning offers assessment of the estimation quality and model selection functionality in a single framework, while as disadvantages, it requires “integral” computation, which often is a bottleneck. In this course, we introduce Bayesian learning, discuss pros and cons, how to perform the integral computation based on “conjugacy”, and how to approximate Bayesian learning when it is intractable.

The course covers

The lecture will be given on a blackboard.

IntroductionBayesianLearningIntroduction.pdf.

Variational Bayesian learning VariationalBayes.pdf.

Markov chain Monte Carlo MCMC_short.pdf.

Summary Summary.pdf.

Homeworks

The students who need credits must do homeworks by the due dates.

IDA Wiki: Main/WS15_BayesianLearning (last edited 2016-01-26 02:16:56 by ShinichiNakajima)