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Explore fundamental machine learning classification techniques through this comprehensive university-level course covering both theoretical foundations and practical applications. Master linear and quadratic discriminant analysis, principal component analysis (PCA), and Fisher's discriminant analysis for dimensionality reduction and feature extraction. Delve into logistic regression and perceptron algorithms, then advance to neural networks including backpropagation and radial basis function networks. Learn about regularization techniques such as Stein's unbiased risk estimator and weight decay methods. Study support vector machines in depth, covering both hard and soft margin approaches, along with dual formulations. Examine supervised learning extensions of PCA and explore tree-based methods including decision trees, k-nearest neighbors, boosting, and bagging ensemble techniques. Discover modern deep learning approaches through convolutional neural networks, understanding their architecture and applications. Conclude with theoretical foundations of machine learning through PAC (Probably Approximately Correct) learning theory, providing mathematical rigor to classification algorithms and their performance guarantees.
