[ Learning mixture models - courseware for finite mixture models of multivariate Bernoulli distributions Jaakko HollménJaakko.Hollmen@tkk.fi Tapani RaikoTapani.Raiko@tkk.fi Department of Information and Computer Science, Helsinki University of Technology
P.O. Box 5400, FI-02015 TKK, Finland
http://www.cis.hut.fi/jhollmen/BernoulliMix ]

Abstract:

Teaching of machine learning should aim at the readiness to understand and implement modern machine learning algorithms. Towards this goal, we often have course exercises involving the student to solve a practical machine learning problem involving a real-life data set. The students implement the programs of machine learning methods themselves and gain deep insight on the implementation details of the method. The downside of this approach is that time is devoted on implementation aspects rather than machine learning. Complementary to this approach, we have designed a machine learning course exercise on a ready implementation of the Expectation-Maximization (EM) algorithm for finite mixture distributions of multivariate Bernoulli distributions. We describe BernoulliMix -- a program package with a set of teaching examples and exercises and report on the preliminary experiences in our class of machine learning students. The BernoulliMix package will be available under a liberal open source license.