Catalog Description: Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods.

Units: 3

Spring: 3 hours of lecture per week
Fall: 3 hours of lecture per week

Grading basis: letter

Final exam status: No final exam

Also listed as: STAT C241A

Class homepage on inst.eecs

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