Covers the Nearest Neighbor search algorithm and the Johnson-Lindenstrauss lemma for dimensionality reduction, exploring preprocessing techniques and locality-sensitive hashing.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.