Schedule is updated dynamically throughout the semester
DATE | TOPIC | READING | OUT | DUE | |
---|---|---|---|---|---|
1 | Tue, Jan 19 | Background & probability review | CB Ch 1-2, Murphy Ch2 | ||
Fri, Jan 22 | Background & probability review continued | Hw1 out | |||
Tue, Jan 26 | Background & probability review continued | ||||
2 | Fri, Jan 29 | 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, Feb 2 | Gaussian one-variable regression: maximum likelihood, gradient descent | CB 3.1.1-3.1.2RW Ch 2, 5.1, 6.3 | |||
Fri, Feb 5 | Multi-variable regression: categorical predictors, unequal variance, robust regression | JW 3.2-3.3 | Hw1 due | ||
Tue, Feb 9 | 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 | ||
Fri, Feb 12 | Regression in high dimensions: curse of dimensionality, regularization | JW 6.1-6.2,HT 3.4.1-3.4.3, CB 3.1.4 | |||
Tue, Feb 16 | Regression in high dimensionscontinued | ||||
4 | Fri, Feb 19 | 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 | ||
Tue, Feb 23 | Logistic regression continued | ||||
Fri, Feb 26 | Exam prep | Hw2Proj group due | |||
Tue, Mar 2 | Midterm exam | ||||
Fri, Mar 5 | Bayes learning, MAP estimation, Naive Bayes | HT 6.6.3,CB 2.3.9; 2.5.2 | |||
Tue, Mar 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 | |||
Fri, Mar 12 | No class. Project pitches 03/16, 6pm | ||||
Tue, Mar 16 | PCA, k-means, EM algorithm, mixtures of Gaussians | JW 10; HT 14.3, 14.5;CB 9.1-9.2 | Hw3 out | Project proposals due 03/17 | |
Fri, Mar 19 | Kernels and kernel density estimation | JW 7.6,HT 6.1-6.3, 6.6, CB 2.5.1 | |||
Tue, Mar 23 | Support vector machines | JW 9.1-4, HT 4.5, 12.1-12.3.4, CB 7.1 | |||
Fri, Mar 26 | Support vector machinescontinued | ||||
Tue, Mar 30 | Classification/regression trees | JW 8.1-8.2,HT 9.2, 15.1-15.3, CB 1.6 | Hw4 out | Hw3 due | |
Fri, Apr 2 | Bagging, random forest;Boosting, model interpretation | JW 8.2HT 10.1-10.2 | |||
Tue, Apr 6 | Neural networks: representation | HT 11.3, CB 5.1-5.2 | |||
Fri, Apr 9 | Neural networks: training | HT 11.4-11.7CB 5.3-5.5.5 | |||
Tue, Apr 13 | Convolutional neural networksGuest lecture byClara De Paolis Kaluza | CB 5.5.6 | |||
Fri, Apr 16 | Exam prep | Hw4 due | |||
Tue, Apr 20 | Final exam | ||||
Fri, Apr 23 | Project presentations | ||||
Mon, Apr 26 | Project reports due | ||||
Wed, Apr 28 | Project reviews due |