Optimization for Machine Learning (Jan 2023)

This contains the details of the tutorial sessions (1.5 hours each) and other duties performed during the TAship.

Tutorial 1: Convexity, convex functions, Lipschitz function, strongly convex, lipchitz smooth functions and assignment 1 discussion.

Tutorial 2: Different gradient descent techniques: Vanilla, analysis of convergence for L-smooth and u-strongly convex functions, projected gradient descent analysis, assignment 2 discussion

Evaluations: 4 assignments, for ~25 students. 2 projects, ~12 groups.