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Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Explores the unique decomposition of matrices into diagonalizable and nilpotent parts, showcasing their properties and applications.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Introduces document classification using features like words and metadata, and models such as k-Nearest-Neighbors and word embeddings.

Covers the LU decomposition method for matrix factorization and its application in electrical engineering.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.