# Topics Description from course guide: "Graduate-level introduction of machine learning, including mathematical derivation and implementation of the algorithms and their applications. Graduate linear algebra and probability are strict prerequisites. Supervised learning, unsupervised learning, kernels, elements of statistical learning theory, graph models, decision trees/random forest, Bayesian techniques and gaussian processes, iterative optimization, KKT conditions, SVMs, expectation maximization, GMMs, PCA, clustering, manifold learning, deep learning. Term project."