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| Statistical Learning Theory -- Computer Science (Engineering) (COMPSCI) C281A [3 units] | ||||
| Course Format: Three hours of lecture per week. | ||||
| Prerequisites: Linear algebra, calculus, basic probability, and statistics, algorithms. Computer Science 289 recommended. | ||||
| 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. Also listed as Statistics C241A. | ||||
| (F) Bartlett, Jordan, Wainwright |
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