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Learning mixture models - courseware for finite mixture distributions of multivariate Bernoulli distributions
Jaakko Hollmen and Tapani Raiko
In: In Proceedings of Teaching Machine Learning - workshop on open problems and new directions, 6 May - 7 May 2008, Saint-Étienne, France.
AbstractTeaching 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 reallife
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. [Edit]
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