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This lecture covers fundamental machine learning concepts such as Structure Discovery through PCA and Clustering, Classification using Bayes Rule, k-NN, and SVM, and Regression with Ordinary Least Squares and SVR. It also delves into model evaluation techniques and exam preparation details.