Schedule

Required readings slides are listed below for each module. Readings from [ISLR] are always required while those from [ESL] are optional and supplemental.

All lecture slides as .qmd files are available here.

R code for all lectures is available here.

Handouts for some lectures (coding files, pdfs) are available here.

Instructions to create .pdfs of the lecture slides (works in Google Chrome or Chromium).

  1. Open some Slides in the browser.
  2. Toggle into Print View by pressing the E key. (It may not appear that anything has happened)
  3. Open the Print dialog: CTRL / CMD + P.
  4. Change the Destination setting to Save as PDF.
  5. Change the Layout to Landscape.
  6. Change the Margins to None.
  7. Enable the Background graphics option.
  8. Click Save 🎉

0 Introduction and Review

Required reading below is meant to reengage brain cells which have no doubt forgotten all the material that was covered in STAT 306 or CPSC 340. We don’t presume that you remember all these details, but that, upon rereading, they at least sound familiar. If this all strikes you as completely foreign, this class may not be for you.

Required reading
[ISLR] 2.1, 2.2, and Chapter 3 (this material is review)
Optional reading
[ESL] 2.4 and 2.6
Handouts
Programming in R .Rmd, .pdf
Using in RMarkdown .Rmd, .pdf
Date Slides Deadlines
05 Sep 23 (no class, Imagine UBC)
07 Sep 23 Intro to class, Git (Quiz 0 due tomorrow)
12 Sep 23 Understanding R / Rmd Lab 00, (Labs begin)
14 Sep 23 LM review, LM Example

1 Model Accuracy

Topics
Model selection; cross validation; information criteria; stepwise regression
Required reading
[ISLR] Ch 2.2 (not 2.2.3), 5.1 (not 5.1.5), 6.1, 6.4
Optional reading
[ESL] 7.1-7.5, 7.10
Date Slides Deadlines
19 Sep 23 Regression function, Bias and Variance
21 Sep 23 Risk estimation, Info Criteria
26 Sep 23 Greedy selection
28 Sep 23 HW 1 due

2 Regularization, smoothing, and trees

Topics
Ridge regression, lasso, and related; linear smoothers (splines, kernels); kNN
Required reading
[ISLR] Ch 6.2, 7.1-7.7.1, 8.1, 8.1.1, 8.1.3, 8.1.4
Optional reading
[ESL] 3.4, 3.8, 5.4, 6.3
Date Slides Deadlines
3 Oct 23 Ridge, Lasso
5 Oct 23 CV for comparison, NP 1
10 Oct 23 NP 2, Why smoothing?
12 Oct 23 No class (Makeup Monday)
17 Oct 23 Other

3 Classification

Topics
logistic regression; LDA/QDA; naive bayes; trees
Required reading
[ISLR] Ch 2.2.3, 5.1.5, 4-4.5, 8.1.2
Optional reading
[ESL] 4-4.4, 9.2, 13.3
Date Slides Deadlines
19 Oct 23 Classification, LDA and QDA
24 Oct 23 Logistic regression, Gradient descent HW 2 due
26 Oct 23 Nonlinear, Other losses
31 Oct 23 The bootstrap

4 Modern techniques

Topics
bagging; boosting; random forests; neural networks
Required reading
[ISLR] 5.2, 8.2, 10.1, 10.2, 10.6, 10.7
Optional reading
[ESL] 10.1-10.10 (skip 10.7), 11.1, 11.3, 11.4, 11.7
Date Slides Deadlines
2 Nov 23 Bagging and random forests, Boosting
7 Nov 23 Intro to neural nets HW 3 due
9 Nov 23 No class. Optional NNet handout
14 Nov 23 No class. (Midterm break)
16 Nov 23 Estimating neural nets
21 Nov 23 Neural nets wrapup HW 4 due

5 Unsupervised learning

Topics
dimension reduction and clustering
Required reading
[ISLR] 12
Optional reading
[ESL] 8.5, 13.2, 14.3, 14.5.1, 14.8, 14.9
Date Slides Deadlines
23 Nov 23 Intro to PCA, Issues with PCA
28 Nov 23 PCA v KPCA
30 Nov 23 K means clustering
5 Dec 23 Hierarchical clustering
7 Dec 23 Review HW 5 due

F Final exam

Monday, December 18 at 12-2pm, SCRF 100

  • In person attendance is required (per Faculty of Science guidelines)
  • You must bring your computer as the exam will be given through Canvas
  • Please arrange to borrow one from the library if you do not have your own. Let me know ASAP if this may pose a problem.
  • You may bring 2 sheets of front/back 8.5x11 paper with handwritten notes you want to use. No other materials will be allowed.
  • There will be no required coding, but I may show code or output and ask questions about it.
  • It will be entirely multiple choice / True-False / matching, etc. Delivered on Canvas.

Schedule of Office Hours before the Final

  • December 11, 5-6pm on Zoom (use the link on Canvas, TA)
  • December 12, 3-4:30 in ESB 4192 (Daniel)
  • December 13, 10-11 in ESB 4192 (TA)
  • December 14, 10-11 in ESB 3174 (Daniel)
  • December 15, 2-3 on Zoom (use the link on Canvas, TA)