Standard syllabus
Machine learning · Graduate · CS / Programming
Topics
Learning theory
- PAC learning framework and VC dimension (intro)
- Bias–variance and regularization paths
- Convex losses and risk minimization
- Generalization bounds overview (intro)
- Model selection and information criteria
Optimization for ML
- Gradient descent, SGD, and momentum methods
- Convex optimization review for ML
- Lagrange multipliers and duality in SVMs
- Proximal methods and sparsity (intro)
- Second-order methods (Newton, L-BFGS intro)
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.