Johns Hopkins University 3 credits Applied Mathematics & Statistics
This course is the second part of a two-semester sequence on Machine Learning. It discusses, in a first part, generative methods in statistics and artificial intelligence, with a short introduction to the theory of Markov chains and Monte-Carlo sampling. It will also address standard unsupervised learning problems, such as dimension reduction, manifold learning and clustering. This content of Machine Learning II is, to a large extent, independent from that of Machine Learning I. Recommended course background: Linear algebra, Multidimensional calculus, Probability (e.g., 553.620).
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