AM207 - Stochastic Methods for Data Analysis, Inference and Optimization

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This is a introductory graduate course on probabilistic modeling and inference for machine learning.

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What is this Course?

The aim of this course is to help students develops skills for computational research with focus on stochastic approaches, emphasizing implementation and examples. Stochastic methods make it feasible to tackle very diverse problems when the solution space is too large to explore systematically, or when microscopic rules are known, but not the macroscopic behavior of a complex system. Methods are illustrated with examples from a wide variety of fields, like demography, health-care, and finance. We tackle Bayesian methods of data analysis as well as various stochastic optimization methods. Topics include stochastic optimization (stochastic gradient descent and simulated annealing), Bayesian models and approximate inference methods (Markov chain Monte Carlo and variational inference).

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