CMSC 422 (Section 0101): Introduction to Machine Learning
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
Lecture 5 (2/9): Nearest Neighbor Classification
Lecture 6 (2/16):Perceptron
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
Lecture 10 (3/2): Gradient Descent Convergence
Lecture 11 (3/7): GD (part II)+ Probabilistic View of ML, Naive Bayes
Lecture 12 (3/9): Logistic Regression + Multilabel Classification
Lecture 13 (3/14): Midterm
Lecture 14 (3/28): Neural Networks
Lecture 15 (4/4): Nonlinear Regression + Back Propagation
Lecture 16 (4/6): Vanishing Gradients, Multi Label Classification, Momentum method
Lecture 17 (4/11): Adversarial Robustness + Recurrent Neural Networks
Lecture 18 (4/13): Unsupervised Learning + KMeans
Lecture 19 (4/18): PCA
Lecture 20 (4/20): AutoEncoders + Kernels
Lecture 21 (4/25): Kernels + SVMs
Lecture 22 (4/27): SVMs II
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
