CMSC 422 (Section 0201): Introduction to Machine Learning
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
Lecture 5 (2/11): Decision Tree+ Entropy
Lecture 6 (2/13): Nearest Neighbor Classification
Lecture 7 (2/18):Perceptron+ Convergence Analysis
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
Lecture 12 (3/5): Midterm
Lecture 13 (3/10): Logistic Regression
Lecture 14 (3/12): Logistic Regression+ Multilabel Classification
Lecture 15 (3/31): Neural Networks
Lecture 16 (4/2): Nonlinear Regression + Back Propagation
Lecture 17 (4/7): Vanishing Gradients, Multi Label Classification, Momentum method
Lecture 18 (4/9): Adversarial Robustness + Recurrent Neural Networks
Lecture 19 (4/14): Unsupervised Learning + KMeans
Lecture 20 (4/16): PCA
Lecture 21 (4/21): AutoEncoders
Lecture 22 (4/23): Kernels
Lecture 23 (4/28): Kernels + Support Vector Machines
Lecture 24 (4/30): SVM + KKT conditions
Lecture 25 (5/5): Support Vector Machines II
