Motivation:

This practical course complements the lecture Machine Learning: Unsupervised Techniques and aims at practicing the concepts and methods acquired in the lecture.

Topics:

• Error models
• Maximum likelihood and the expectation maximization algorithm
• Maximum entropy methods
• Basic clustering methods, hierarchical clustering, and affinity propagation
• Mixture models
• Principal component analysis, independent component analysis, and other projection methods
• Factor analysis
• Matrix factorization
• Auto-associator networks and attractor networks
• Boltzmann and Helmholtz machines
• Hidden Markov models
• Belief networks
• Factor graphs