# COS 598D: Optimization for Machine Learning

# Course Description & Basic Information

Course Description & Basic Information

**Professor: Elad Hazan**

The course address optimization problems that arise in machine learning, as well as efficient algorithms to solve them. The course is proof-based, and contains both theory and applied exercises (choice given).

**Topic covered:**

- Introduction to convex analysis
- first-order methods, convergence analysis
- generalization and regret minimization
- regularization
- gradient descent++:
- acceleration
- variance reduction
- adaptive preconditioning

- 2nd order methods in linear time
- projection-free methods and the Frank-Wolfe algorithm
- zero-order optimization, convex bandit optimization
- optimization for deep learning: large scale non-convex optimization

### Lectures

Lectures

Tuesdays 10:00-12:20, in Computer Science Building Rm 402

Tuesdays 09:00-10:00, self-study in COS 402 and lecture preparation / office hours (optional)

**Professors' office hours: ** Mon 9-10am in COS 409 or COS 402