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.
    [notes] [slides]

    Readings:

    • PML: Chapter 1
    • PRML: Chapter 1
  • ML Foundations
    tl;dr: Generalization: overfitting, bias-variance, regularization (L2/L1).
    [notes] [slides]

    Readings:

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

    Readings:

    • PML: Chapter 11.1-11.4
  • Optimization Basics
    tl;dr: Convexity, stochatsic gradient descent, regularization techniques.
    [notes] [slides] [Matrix Calculus] [Jupyter Notebook]

    Readings:

    • PML: Chapter 8.1, 8.2, 8.4
  • Logistic Regression
    tl;dr: Classification, linear classifier, maximum likelihood.
    [notes] [slides]
  • 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.

  • Kernels
    tl;dr: feature maps, kernel functions, and beyond SVM.