Schedule is updated dynamically throughout the semester
| DATE | TOPIC | READING | OUT | DUE | |
|---|---|---|---|---|---|
| 1 | Fri, Jan 9 | Background & probability review | KM Ch2-3.2 | ||
| Tue, Jan 13 | Background & probability review continued | KM Ch2-3.2 | Hw1 out | ||
| Fri, Jan 16 | Statistical learning; 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 | |||
| Tue, Jan 20 | Gaussian one-variable regression: maximum likelihood, gradient descent | CB 3.1.1-3.1.2JW Ch 2, 5.1, 6.3 | |||
| Fri, Jan 23 | Gradient descent, continued | ||||
| Tue, Jan 27 | Multi-variable regression: categorical predictors, unequal variance, robust regression | JW 3.2-3.3 | Hw1 due | ||
| Fri, Jan 30 | 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 | ||
| Tue, Feb 3 | Guest lecture: Prof. Eric Gerber Regression in high dimensions: curse of dimensionality, regularization | JW 6.1-6.2,HT 3.4.1-3.4.3, CB 3.1.4 | |||
| Fri, Feb 6 | Guest lecture: Prof. Eric Gerber Regression in high dimensionscontinued | ||||
| Tue, Feb 10 | 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 | Proj group due | ||
| Fri, Feb 13 | Logistic regression continued | ||||
| 2 | Tue, Feb 17 | Bayes learning, MAP estimation, Naive Bayes | HT 6.6.3,CB 2.3.9; 2.5.2 | ||
| Fri, Feb 20 | LDA, regularized LDA, diagonal LDA, nearest shrunken centroids | JW 4.4, HT 4.3, 18.2CB 4.1.1-4.1.6, 4.2 | Hw2 due | ||
| Tue, Feb 24 | Midterm exam(Cancelled, snow day instead) | ||||
| Fri, Feb 27 | Project pitches(Postponed, midterm instead) | ||||
| Tue, Mar 3 | No class, Spring break | ||||
| Fri, Mar 6 | No class, Spring break | ||||
| Mon, Mar 9 | Project pitches on zoom | ||||
| Tue, Mar 10 | PCA, k-means, EM algorithm, mixtures of Gaussians | JW 10; HT 14.3, 14.5;CB 9.1-9.2 | |||
| 3 | Fri, Mar 13 | PCA continued | Hw3 out | ||
| Tue, Mar 17 | Kernels and kernel density estimation | JW 7.6,HT 6.1-6.3, 6.6, CB 2.5.1 | Project proposals due | ||
| Fri, Mar 20 | Support vector machines | JW 9.1-4, HT 4.5, 12.1-12.3.4, CB 7.1 | |||
| Tue, Mar 24 | Support vector machines continued | ||||
| Fri, Mar 27 | Classification/regression trees | JW 8.1-8.2,HT 9.2, 15.1-15.3, CB 1.6 | Hw4 out | Hw3 due | |
| Tue, Mar 31 | Bagging, random forest;Boosting, model interpretation | JW 8.2HT 10.1-10.2 | |||
| 4 | Fri, Apr 3 | Neural networks: representation | HT 11.3, CB 5.1-5.2 | ||
| Tue, Apr 7 | Neural networks: training | HT 11.4-11.7CB 5.3-5.5.5 | |||
| Fri, Apr 10 | Convolutional neural networks | CB 5.5.6 | Hw4 due | ||
| Tue, Apr 14 | Final exam | ||||
| Fri, Apr 17 | Project presentationsOverflow 2-3:30pm on zoom | ||||
| Mon, Apr 20 | Project reports due | ||||
| Wed, Apr 22 | Project reviews due |