Description:
When do machine learning algorithms work and why? How do we formalize what it means for an algorithm to learn from data? How do we use mathematical thinking to design better machine learning methods?
This course focuses on developing a theoretical
understanding of the statistical properties of learning algorithms.
Topics:
- Generalization bounds via uniform convergence
- Theory for deep learning
- Non-convex optimization
- Neural tangent kernel
- Implicit/algorithmic regularization
- Unsupervised learning and domain adaptation
- Bandit and online earning (if time permits)
