COMS 4995-1 Spring 2020 (Machine Learning Theory)
Lecture & reading schedule
Statistical learning
- Overview (1/22)
- Concentration of measure (1/27, 1/29)
- Statistical learning (2/3, 2/5)
- Empirical process theory (2/10, 2/12, 2/17, 2/19, 2/24, 2/26)
- Boosting and margins (2/26, 3/2, 3/4)
- Convex surrogate losses (3/11, 3/30)
- Approximation theory (3/30, 4/1)
Online learning
- Online learning with experts (4/6, 4/8)
- Multi-armed bandits (4/13, 4/15)
- Online convex optimization (4/20)
Unsupervised learning
- Mixtures of Gaussians (4/22)
- Best-fit subspaces (4/27, 4/29)
- Spectral clustering (4/29, 5/4)