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)
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$1,162 · Machine learning · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.