Introduction to machine learning. Nearest neighbors method. Linear regression. Polynomial regression. RBF neural networks. Logistic regression and Bayesian variant. Kernel methods. Neural networks. Model selection. Main Component Analysis. Autoencoders. k-means. Mixture of Gaussians from the EM algorithm.
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