Welcome to the CMSC 422 course webpage for Spring 2022.
Lecture 1 (1/25): Welcome to Machine Learning + ERMs
Lecture 4 (1/27): Decision Trees
Lecture 3 (2/1): Nearest Neighbor Classification
Lecture 4 (2/3):Perceptron
Lecture 5 (2/8): Convergence Analysis of Perceptron
Lecture 6 (2/10): Linear Classifiers, Gradient Descent and Hinge Loss
Lecture 7 (2/15): Gradient Descent Part II
Lecture 8 (2/17): GD (part III)+ Probabilistic View of ML, Naive Bayes
Lecture 9 (2/22): Review of Probability, Linear Algebra and Convex Analysis
Lecture 10 (2/24): Review of Probability, Linear Algebra and Convex Analysis
Lecture 11 (3/1): Probabilistic View of ML, Naive Bayes
Lecture 12 (3/3): Midterm
Lecture 13 (3/8): Logistic Regression + Multi-label Classification
Lecture 14 (3/10): Neural Networks
Lecture 15 (3/15): Nonlinear Regression + Back Propagation
Lecture 16 (3/17): Back Propagation
Lecture 17 (3/29): Vanishing Gradients, Multi Label Classification, Momentum method
Lecture 18 (3/31): Adversarial Robustness + Recurrent Neural Networks
Lecture 19 (4/5): Unsupervised Learning + K-Means
Lecture 20 (4/7): PCA
Lecture 21 (4/12): PCA analysis
Lecture 22 (4/14): AutoEncoders
Lecture 23 (4/18): Kernels
Lecture 24 (4/21): Kernels II
Lecture 25 (4/26): SVMs
Lecture 26 (4/28): SVMs II + KKT Conditions
Lecture 27 (5/3): SVMs III + Course Review
Lecture 28 (5/5): Final Presentations I
Lecture 29 (5/10): Final Presentations II