Advanced Probabilistic Machine Learning


Advanced concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models and applications. Topics include nonparametric Bayesian methods, optimization, sparsity, topic models, ranking, social network analysis, and more. Prerequisites: Linear algebra and STA250 or STA611 required. STA561D recommended. One course.

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