Machine Learning Module Wiki

Map of the module’s weekly themes and key concepts.

Weeks

week 01 — What Does It Mean to Learn from Data?
week 02 — Solving Logistic Regression: From MLE to IRLS
week 03 — Beyond the Line: Basis Expansion and Maximum Margin
week 04 — Going Dual: Lagrangians, Kernels, and the Trick That Makes Them Tractable
week 05 — Allowing Mistakes: Soft Margins and How to Actually Solve the QP
week 06 —
week 07 — Linear Regression and Why Squared Error Isn’t Arbitrary
week 08 — From Bayesian Priors to Generalization Bounds
week 09 —
week 10 —
week 11 —
week 12 —

optimization algorithms — GD vs Newton-Raphson/IRLS vs SMO: when each is the right tool, and why
classification approaches — Logistic regression vs hard-margin SVM vs soft-margin SVM: same hypothesis, different criteria