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