CMSC 422 (Section 0101): Introduction to Machine Learning

University of Maryland, College Park, Spring 2023
Instructor: Soheil Feizi

Welcome to the CMSC 422 course webpage for Spring 2023.

CMSC 422 (Section 0101) is currently being taught by Soheil Feizi. See UMD Web Accessibility.

Announcements

  • Lecture 1 (1/25): Welcome to Machine Learning + ERMs

  • Lecture 2 (1/31): Review of Probability and Linear Algebra.

  • Lecture 3 (2/2): Review of Probability and Linear Algebra.

  • Lecture 4 (2/7): Decision Trees

    • Reading: Chapter 1 of the text book

    • Slides

  • Lecture 5 (2/9): Nearest Neighbor Classification

    • Reading: Chapter 3 of the text book

    • Slides

  • Lecture 6 (2/16):Perceptron

    • Reading: Chapter 4 of the text book

    • Slides

  • Lecture 7 (2/21): Convergence Analysis of Perceptron

  • Lecture 8 (2/23): Linear Classifiers, Gradient Descent and Hinge Loss

  • Lecture 9 (2/28): Gradient Descent

    • Reading: Chapter 7 of the text book

    • Slides

  • Lecture 10 (3/2): Gradient Descent Convergence

  • Lecture 11 (3/7): GD (part II)+ Probabilistic View of ML, Naive Bayes

    • Reading: Chapter 9 of the text book

    • Slides

  • Lecture 12 (3/9): Logistic Regression + Multi-label Classification

  • Lecture 13 (3/14): Midterm

  • Lecture 14 (3/28): Neural Networks

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 15 (4/4): Nonlinear Regression + Back Propagation

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 16 (4/6): Vanishing Gradients, Multi Label Classification, Momentum method

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 17 (4/11): Adversarial Robustness + Recurrent Neural Networks

  • Lecture 18 (4/13): Unsupervised Learning + K-Means

  • Lecture 19 (4/18): PCA

  • Lecture 20 (4/20): AutoEncoders + Kernels

    • Slides

    • Reading: Chapter 11 of the text book

  • Lecture 21 (4/25): Kernels + SVMs

    • Slides

    • Reading: Chapter 7 of the text book

  • Lecture 22 (4/27): SVMs II

    • Slides

    • Reading: Chapter 7 of the text book

  • Lecture 23 (5/2): Final Presentations I

  • Lecture 24 (5/4): Final Presentations II

  • Lecture 25 (5/9): Review

  • Lecture 26 (5/11): Final Presentations III