# Optimization for Machine Learning

Below you can find slides and lecture notes. The syllabus can be found here, the homework here and the projects here.

Lecture 1 slides, Lecture notes: Definitions and Gradient Descent

Lecture 2 slides, Lecture notes: Stochastic Gradient Descent

Lecture 3 slides: Stochastic Gradient Descent (part 2)

Lecture 4 slides, Lecture notes: Online optimization and Online Learning

Lecture 5 slides, Lecture notes: Non-convex optimization

Lecture 6 slides: Non-convex optimization (part 2)

Lecture 7 slides, Lecture notes: Accelerated GD (Nesterov)

Lecture 8 slides, Lecture notes: Intro to min-max optimization

Lecture 9 slides: Min-max optimization using optimism

Lecture 10 slides, Lecture notes: Intro to statistical learning and ERM

Lecture 11 slides: VC dimension

Lecture 12 slides, Lecture notes: Intro to multi-armed bandits

Lecture 13 slides: Intro to multi-armed bandits part 2

Lecture 14 slides: Intro to MDPs