CMSC 422 (Section 0201): Introduction to Machine Learning

University of Maryland, College Park, Spring 2020
Instructor: Soheil Feizi

Welcome to the CMSC 422 course webpage for Spring 2020.

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

Announcements

  • Lecture 1 (1/28): Welcome to Machine Learning + ERMs

  • Lecture 2 (1/30): Review of Probability and Linear Algebra.

  • Lecture 3 (2/4): No lecture

  • Lecture 4 (2/6): Decision Trees

    • Reading: Chapter 1 of the text book

    • Slides

  • Lecture 5 (2/11): Decision Tree+ Entropy

  • Lecture 6 (2/13): Nearest Neighbor Classification

    • Reading: Chapter 3 of the text book

    • Slides

  • Lecture 7 (2/18):Perceptron+ Convergence Analysis

    • Reading: Chapter 4 of the text book

    • Slides

  • Lecture 8 (2/20): Linear Classifiers, Gradient Descent and Hinge Loss

  • Lecture 9 (2/25): Gradient Descent, Analysis

  • Lecture 10 (2/27): Stochastic GD

  • Lecture 11 (3/3): Probabilistic View of ML + Naive Bayes

    • Reading: Chapter 9 of the text book

    • Slides

  • Lecture 12 (3/5): Midterm

  • Lecture 13 (3/10): Logistic Regression

  • Lecture 14 (3/12): Logistic Regression+ Multi-label Classification

  • Lecture 15 (3/31): Neural Networks

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 16 (4/2): Nonlinear Regression + Back Propagation

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 17 (4/7): Vanishing Gradients, Multi Label Classification, Momentum method

    • Slides

    • Reading: Chapter 10 of the text book

  • Lecture 18 (4/9): Adversarial Robustness + Recurrent Neural Networks

  • Lecture 19 (4/14): Unsupervised Learning + K-Means

  • Lecture 20 (4/16): PCA

    • Slides

    • Reading: Chapter 15 of the text book

  • Lecture 21 (4/21): AutoEncoders

    • Slides

    • Reading: Chapter 11 of the text book

  • Lecture 22 (4/23): Kernels

    • Slides

    • Reading: Chapter 11 of the text book

  • Lecture 23 (4/28): Kernels + Support Vector Machines

    • Slides

    • Reading: Chapter 7 of the text book

  • Lecture 24 (4/30): SVM + KKT conditions

    • Slides

    • Reading: Chapter 7 of the text book

    • Reading: notes

  • Lecture 25 (5/5): Support Vector Machines II

    • Slides

    • Reading: Chapter 7 of the text book

    • Reading: notes