Lecture 1: Introduction

Lecture 2: Review of basic math, LEC 1, and Linear models

Lecture 3: Cross-Validation and Regularization

Lecture 4: Gradient, Classification, and Logistic Regression.

Lecture 5: Confusion Matrix, Multi-class Logistic Regression, Convex Functions.

Lecture 6: Gaussian Discriminant Analysis, Naive Bayes

Lecture 7: Moving beyond linearity