This lecture covers Gaussian discriminant analysis, focusing on the multivariate normal distribution, mean, and covariance matrix. It also discusses the log-likelihood of data, supervised learning instances, and logistic regression.
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This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topi
