Schedule
Topics for each class day, as well as links to the pre-class videos and slides, are listed below.
Before each class, watch the pre-class video and answer the corresponding comprehension questions on Moodle.
Tentative overall schedule
Regression tasks: Weeks 2-5
- Topics: model evaluation, model building, nonparametric methods, tools for modeling nonlinearity
Classification tasks: Weeks 6-9
- Topics: model evaluation, logistic regression, tree-based methods, support vector methods
Unsupervised learning: Weeks 10-11
- Topics: principal components analysis, clustering
Other topics + project work time: Weeks 12-14
- Suggested topic: deep learning
Week 2: 1/28 - 2/1
- Monday: Assumptions of linear regression (Video, Slides)
Related ISLR reading: Section 2.1 gives some general background that is not particular to linear regression assumptions but sets up some key foundational ideas. - Friday: Model evaluation metrics for regression (Video, Slides)
Related ISLR reading: Sections 2.2.1-2.2.2 talk about MSE, training and test data, test error, cross-validation, and the bias-variance tradeoff. The bias-variance tradeoff will come up a little later in class, but feel free to preview the ideas now.
Week 3: 2/4 - 2/8
- Monday: Cross-validation (Video, Slides)
Related ISLR reading: Section 5.1 is devoted to cross-validation. See also Sections 2.2.1-2.2.2. - Wednesday: We’ll finish reviewing cross validation, then talk briefly about variable selection methods for building models (Video, Slides)
Related ISLR reading: Section 6.1 - Friday: Quiz 1. Remaining time will be left for working on homework.
Week 4: 2/11 - 2/15
- Monday: Shrinkage/regularization methods for model building (Video, Slides)
Related ISLR reading: Section 6.2 - Wednesday: Continue discussing shrinkage methods
- Friday: K-nearest neighbors regression and the bias-variance tradeoff (Video, Slides)
Related ISLR reading: Section 2.2.2 for the bias-variance tradeoff and Section 3.5 for K-nearest neighbors regression
Week 5: 2/18 - 2/22
Week 6: 2/25 - 3/1
- Monday: Logistic regression (Video, Slides)
Related ISLR reading: Sections 4.1-4.3 - Wednesday: Finish up logistic regression. Using old tools in the classification setting
Related ISLR reading: Section 7.7.2 (GAMs for Classification Problems), pages 39-42 (KNN for classification) - Friday: Decision trees (Video, Slides)
Related ISLR reading: 8.1
Week 7: 3/4 - 3/8
Week 8: 3/11 - 3/15
Review & midterm exam
Happy Spring Break!
Week 9: 3/25 - 3/29
Week 10: 4/1 - 4/5
- Monday: Continue with support vector machines
- Wednesday: K-means clustering (Video, Slides)
Related ISLR reading: Section 10.3 (specifically 10.3.1 for K-means) - Friday: Quiz 4. Hierarchical clustering (Video, Slides)
Related ISLR reading: Section 10.3 (specifically 10.3.2 for hierarchical clustering)
Week 11: 4/8 - 4/12
Week 12: 4/15 - 4/19
- This week is an introduction to deep learning: Slides