# CS 295 - Optimization for Machine Learning

Below we will be posting slides and lecture notes for the class. The syllabus can be found here.

* NEW! * Lecture notes are available here.

Lecture 1 slides: Definitions and Gradient Descent

Lecture 2 slides: Gradient Descent (cont.)

Lecture 3 slides: Stochastic Gradient Descent

Lecture 4 slides: Stochastic Gradient Descent (examples)

Lecture 5 slides: Intro to Online optimization and Online Learning

Lecture 6 slides: Online optimization and Online Learning (cont.)

Lecture 7 slides: Accelerated Methods

Lecture 8 slides: Intro to non-convex optimization

Lecture 9 slides: Non-convex optimization: GD + noise avoids saddles

Lecture 10 slides: Introduction to min-max

Lecture 11 slides: Min-max and optimism

Lecture 12 slides: Intro to Multi-Armed Bandits

Lecture 13 slides: Multi-Armed Bandits (part 2)

Lecture 14 slides: Intro to MDPs

Lecture 17 slides: Stochastic Games and multi-agent RL

Lecture 15 slides: Intro to Perceptron and ERM

Lecture 16 slides: VC dimension and Learnability