Foundations for analyzing and developing intelligent systems with essential concepts in linear algebra, probability, calculus, optimization, and statistical inference. Estimation and decision theory. Regression and regularization. Optimization. Dimensionality reduction. Matrix/tensor decomposition and efficient implementations. Monte Carlo sampling. Graphical models and efficient algorithms. Variational inference. Performance evaluation. Emphasis on theoretical understanding as well as computational applications in computer science.
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