Schedule is updated dynamically during the semester
| DATE | TOPIC | READING | OUT | DUE | |
|---|---|---|---|---|---|
| 0 | Background | CB 1-2 | |||
| 1 | Fri, Sep 7 | Statitical learning; k-NN regression, one-variable linear regression, least squares, regression vs correlation | JW 2.1;3.1HT 2.1-2.4, 2.6, 3.2-3.2.2 | Hw1 out | |
| 2 | Tue, Sep 11 | Gaussian one-variable regression: maximum likelihood, gradient descent, measures of associations | CB 3.1.1-3.1.2RW Ch 2, 5.1, 6.3 | ||
| 3 | Fri, Sep 14 | Multi-variable regression: categorical predictors, unequal variance, robust regression | JW 3.2-3.3 | ||
| 4 | Tue, Sep 18 | Overfitting, bias/variance tradeoff, variable selection, cross-validation | JW 5.1, 6.1,HT 3.3, 7.2-7.3, 7.10, CB 3.2 | Hw2 out | Hw1 due |
| 5 | Fri, Sep 21 | Regression in high dimensions: curse of dimensionality, regularization | JW 6.1-6.2,HT 3.4.1-3.4.3, CB 3.1.4 | ||
| 6 | Tue, Sep 25 | Regression in high dimensions continued | |||
| 7 | Fri, Sep 28 | k-NN, perceptron, logistic regression, multinomial/softmax regression | JW 4.2-4.3, HT 4.2-4.4CB 2.2, 4.1.7, 4.3 | ||
| 8 | Tue, Oct 2 | Guest lecture | Hw3 out | Hw2 dueProj. group due | |
| 9 | Fri, Oct 5 | Bayes learning, MAP estimation, Naive Bayes, mixtures of Gaussians | HT 6.6.3,CB 2.3.9; 2.5.2 | ||
| 10 | Tue, Oct 9 | LDA, regularized LDA, diagonal LDA, nearest shrunken centroids | JW 4.4, HT 4.3, 18.2CB 4.1.1-4.1.6, 4.2 | ||
| 11 | Fri, Oct 12 | Bayesian inference for Gaussian models and regularization; information theory | CB 1.6,2.3.5-2.3.6, 3.3.1 | ||
| 12 | Tue, Oct 16 | Exam prep | Hw3 due | ||
| 13 | Fri, Oct 19 | Midterm | |||
| 14 | Tue, Oct 23 | Basis expansion, splines, kernels; kernel density estimation | JW 7.1-7.6,HT 5.1-5.5, 6.1-6.3, 6.6, CB 2.5.1 | ||
| 15 | Fri, Oct 26 | Basis expansion, continued | |||
| 16 | Tue, Oct 30 | In-class project pitches | Hw4 out | Proj. pitch due | |
| 17 | Fri, Nov 2 | Support vector machines, convex optimization | JW 9.1-4, HT 4.5, 12.1-12.3.4, CB 7.1 | ||
| 18 | Tue, Nov 6 | Classification/regression trees, bagging, random forest;Boosting, model interpretation | JW 8.1-8.2,HT 9.2, 15.1-15.3JW 8.2 HT 10.1-10.2 | ||
| 19 | Fri, Nov 9 | Neural networks: representation | HT 11.3, CB 5.1-5.2 | Hw5 out | Hw4 due |
| 20 | Tue, Nov 13 | Guest lecture | |||
| 21 | Fri, Nov 16 | Neural networks: training | HT 11.4-11.7CB 5.3-5.5.5 | ||
| 22 | Tue, Nov 20 | Convolutional neural networks | CB 5.5.6 | ||
| 23 | Fri, Nov 23 | No class - Thanksgiving | |||
| 24 | Tue, Nov 27 | Exam prep | Hw5 due | ||
| 25 | Fri, Nov 30 | Final exam | |||
| 26 | Tue, Dec 4 | Project presentations | |||
| 27 | Fri, Dec 7 | Project presentations | Proj. report due | ||
| 28 | Tue, Dec 11 | Finals week | Proj. review due |