The schedule is updated dynamically during the semester
| DATE | TOPIC | READING | OUT | DUE | |
|---|---|---|---|---|---|
| 0 | Background | CB 1-2 | |||
| 1 | Tue, Jan 8 | Statistical 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 | Fri, Jan 11 | Gaussian one-variable regression: maximum likelihood, gradient descent | CB 3.1.1-3.1.2RW Ch 2, 5.1, 6.3 | ||
| 3 | Tue, Jan 15 | Gaussian regression and gradient descent continued | |||
| 4 | Fri, Jan 18 | Covex functions | Hw2 out | Hw1 due Jan 19 | |
| 5 | Tue, Jan 22 | Multi-variable regression: categorical predictors, unequal variance, robust regression | JW 3.2-3.3 | ||
| 6 | Fri, Jan 25 | 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 | ||
| 7 | Tue, Jan 29 | Regression in high dimensions: curse of dimensionality, regularization | JW 6.1-6.2,HT 3.4.1-3.4.3, CB 3.1.4 | Hw3 out | Hw2 due Jan 30 |
| 8 | Fri, Feb 1 | Regression in high dimensionscontinued | |||
| 9 | Tue, Feb 5 | Guest lecture | |||
| 10 | Fri, Feb 8 | Exam prep | Hw3 dueProj. group due | ||
| 11 | Tue, Feb 12 | Midterm | |||
| 12 | Fri, Feb 15 | 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 | Hw4 out | |
| 13 | Tue, Feb 19 | Logistic regression continued | |||
| 14 | Fri, Feb 22 | Bayes learning, MAP estimation, Naive Bayes, mixtures of Gaussians | HT 6.6.3,CB 2.3.9; 2.5.2 | ||
| 15 | Tue, Feb 26 | LDA, regularized LDA, diagonal LDA, nearest shrunken centroids | JW 4.4, HT 4.3, 18.2CB 4.1.1-4.1.6, 4.2 | ||
| 16 | Fri, Mar 1 | Kernels and kernel density estimation | JW 7.6,HT 6.1-6.3, 6.6, CB 2.5.1 | Hw4 due | |
| 17 | Tue, Mar 5 | No class - Spring break | |||
| 18 | Fri, Mar 8 | No class - Spring break | |||
| 19 | Tue, Mar 12 | In-class project pitches | Proj. pitch due | ||
| 20 | Fri, Mar 15 | Support vector machines | JW 9.1-4, HT 4.5, 12.1-12.3.4, CB 7.1 | Hw5 out | |
| 21 | Tue, Mar 19 | Support vector machinescontinued | |||
| 22 | Fri, Mar 22 | Classification/regression trees | JW 8.1-8.2,HT 9.2, 15.1-15.3, CB 1.6 | ||
| 23 | Tue, Mar 26 | Bagging, random forest;Boosting, model interpretation | JW 8.2HT 10.1-10.2 | Hw5 due | |
| 24 | Fri, Mar 29 | Neural networks: representation | HT 11.3, CB 5.1-5.2 | Hw6 out | |
| 25 | Tue, Apr 2 | Guest lectureNeural networks: training | HT 11.4-11.7CB 5.3-5.5.5 | ||
| 26 | Fri, Apr 5 | Convolutional neural networks | CB 5.5.6 | ||
| 27 | Tue, Apr 9 | Exam prep | Hw6 due | ||
| 28 | Fri, Apr 12 | Final exam | |||
| 29 | Tue, Apr 16 | Project presentations | |||
| 30 | Fri, Apr 19 | Project presentations | Proj. report due | ||
| 31 | Tue, Apr 23 | Finals week | Proj. review due |