COMS 4771-002 Fall 2018 (Machine Learning)

Essentials


Homework assignments and exams


Lecture & reading schedule

Dates Lecture topic Reading assignment
9/5 Overview CML 1.1-1.2; PC 1.1-1.6.
9/5 Nearest neighbor classifiers CML 3.1-3.3; PC 4.5.5, 4.6.
9/10 Predictions CML 9.1-9.2; PC 3.2; coin tosses handout.
9/12, 9/17 Generative models CML 9.3-9.5; PC 2.4-2.6, 4.4.
9/19 Risk estimation, model selection/averaging CML 5.6; optional: prediction theory tutorial.
9/24, 9/26 Linear regression CO 3.1.1, 3.1.3-3.1.5, 3.1.8-3.1.9, 3.2.1-3.2.4; linear regression handout.
10/1, 10/3 Logistic regression and linear classifiers CML 4.1-4.7; 5.1, 5.3-5.4; PC 5.1-5.3; Perceptron handout; online-to-batch handout.
10/8, 10/10 Support vector machines CML 7.7, 11.1-11.2, 11.4-11.6.
10/22, 10/24, 10/29 Generalization theory CML 12.1-12.4; optional: statistical learning theory tutorial.
10/29 Convex optimization CML 7.1-7.3; CO 2.1.1-2.1.4, 2.2-2.3.2, 4.2.1-4.2.3.
10/31 Optimization algorithms CML 7.4, 14.2; PC 5.4; CO 9.2-9.3; stochastic gradient tricks; gradient descent demo, stochastic gradient method demo.
11/7 Neural networks CML 10.1-10.5; PC 6.1-6.3; efficient backprop; multi-layer networks handout.
11/12 No lecture PyTorch 60 Minute Blitz tutorial
11/14 Classification objectives CML 5.5, 6.1-6.2, 8.1; one-against-all handout.
11/19 Ensemble methods BFA 1.1-1.3, 3.4.3; CML 1.3-1.6, 13.1-13.2; PC 8.3, 9.4.2, 9.5.1-9.5.2; AdaBoost handout.
11/26 Clustering CML 3.4-3.5, 15.1; PC 10.9.
11/28 Societal consequences CML 8.4; how big data is unfair; ProPublica article on COMPAS.
12/3 Principal components analysis CML 15.2; notes on PCA 5.1, 5.2, 5.4.