You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • Overview
    tl;dr: Course overview, syllabus, and introduction to machine learning.

  • ML Foundations
    tl;dr: Generalization: overfitting, bias-variance, regularization (L2/L1).

  • Linear Regression
    tl;dr: Least squares, gradient descent, Ridge and Lasso.

  • Optimization Basics
    tl;dr: Convexity, stochatsic gradient descent, regularization techniques.

  • Logistic Regression
    tl;dr: Classification, linear classifier, maximum likelihood.

  • Support Vector Machines (I)
    tl;dr: Maximum margin classifier, Hard margin SVM.

  • Support Vector Machines (II)
    tl;dr: Soft margin SVM, duality, KKT condistions, kernel tricks.