Lectures related to Chebyshev distance

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Covers orthogonal vectors, unit vectors, and the Pythagorean theorem in R^m.

Covers convex optimization problems, transformation to standard form, and optimality criteria for differentiable objectives.

Explains the K-Nearest Neighbors classifier, assigning labels based on closest points and smoothing noise in labels.