Slides Lab Topic Chapters Date
Lecture 1 Class logistics, HW 0 Mon 9/23
Lecture 2 Lab 2 Supervised and unsupervised learning 2 Wed 9/25
Lecture 3 Classification, Principal components analysis 10.1,10.2,10.4 Fri 9/27
Lecture 4 PCA, Clustering 10.3,10.5 Mon 9/30
Lecture 5 Linear regression 3.1-3.2 Wed 10/02
Lecture 6 Lab 6 Linear regression 3.3 Fri 10/04
Lecture 7 Linear regression 3.5 Mon 10/07
Lecture 8 Lab 8 Linear regression, Classification: logistic regression 4.1-4.3 Wed 10/09
Lecture 9 Classification: LDA, QDA 4.4-4.5 Fri 10/11
Lecture 10 Classification examples 4.6 Mon 10/14
Lecture 11 Lab 11 Cross validation 5.1, ESL 7.10 Wed 10/16
Lecture 12 Lab 12 The Bootstrap 5.2 Fri 10/18
Lecture 13 Lab 13 Model selection 6.1, 6.2.1 Mon 10/21
Lecture 14 Lab 14 Model selection and regularization 6.2 Wed 10/23
Lecture 15 Dimensionality reduction 6.3, 6.4 Mon 10/28
Lecture 16 High-dimensional and non-linear regression, splines 6.4, 7.1-7.4 Wed 10/30
Lecture 17 Lab 17 Smoothing splines, GAMS, Local regression 7.5-7.7 Fri 11/01
Lecture 18 Lab 18 GAMs, Document analysis 7.7 Mon 11/04
Lecture 19 Lab 19 Decision trees 8.1 Wed 11/06
Lecture 20 Lab 20 Classification trees, Bagging, Random forests 8.1, 8.2 Fri 11/08
Lecture 21 Boosting, Support vector classifiers 9.1, 9.2 Mon 11/11
Lecture 22 Support vector machines 9.3, 9.4 Wed 11/13
Lecture 23 Support vector machines 9 Fri 11/15
Lecture 24 Non-linear dimensionality reduction ESL 14.5.4, 14.8-9 Mon 11/18
Lecture 25 Lab 25 Missing data ESL 9.6 Wed 11/20
Lecture 26 Lab 25 ESL 9.6 Fri 11/22
Lecture 27 Relational data Mon 12/2
Lecture 28 Review Wed 12/4