Welcome to cs1420!
How can artificial systems learn from examples, and discover information buried in massive datasets? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include empirical risk minimization, probably approximately correct learning, maximum likelihood parameter estimation, kernel methods, neural networks, the expectation maximization algorithm, and principal component analysis.
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