Linear regression. Maximum likelihood and maximum a posteriori estimation. Classification techniques, including na?ve Bayes, k-nearest neighbors, logistic regression, and support vector machines. Data visualization and feature extraction, including principal components analysis and linear projections. Clustering. Introduction to neural networks and deep learning. Discussion of other modern analysis methods.

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