Lecture and Lab Schedule

Lectures have been created using Quarto which includes a built in version of the reveal.js-menu plugin. You can access the navigation menu using the button located in the bottom left corner of the presentation1. Clicking the button opens a slide navigation menu that enables you to easily jump to any slide.

Print/Save to PDF:

Reveal presentations can be exported to PDF via a special print stylesheet.

  1. Toggle into Print View using the E key (or using the Navigation Menu)
  2. Open the in-browser2 print dialog (CTRL/CMD+P).
  3. Change the Destination setting to Save as PDF.
  4. Change the Layout to Landscape.
  5. Change the Margins to None.
  6. Enable the Background graphics option.
  7. Click Save 🎉

Schedule

Lab Schedule
Lecture Topic Supplementary Reading
1 Welcome! Introduction To R and RStudio
2 Notation and Terminology ISLR Ch 1
Lab 0: A refresher on R and introduction to Quarto documents
3

Assessing Regression Models

-MSE and Testing vs. Training MSE
- Decomposition of MSE
- Reducible error and Irreducible Error
- Bias-Variance Tradeoff

 ISLR 2.2.1, 2.2.2
4 Linear Regression ISLR Section 3.1, 3.2
Lab 1: Assessing Regression Models ISLR 2.2
5

Extensions to the linear regression model: Interaction, Categorical Predictors, Polynomial regression.

KNN Regression (non-parametric approach)

 ILSR Section 3.3, 3.4, 3.5, Lab 3.6
Lab 2: Regression Diagnostics and Predictive Modeling: Exploring Linear, Polynomial, and KNN Regression
6 Logistic Regression ISLR Section 4.1, 4.2, 4.3; ESL 4.4; ILSR Section 2.2.3 (for assessing classification models)
7 Classification models: Bayes Classifier, KNN Classification and Discriminant Analysis ILSR Sections 2.2.3 and 4.4.1, 2, 3; ESL 4.3
Lab 3: Classification Models: Fitting and Evaluating using Test Error and Confusion Matrices
8 Cross Validation ILSR 5.1
9 Bootstrap ILSR 5.2, 5.3
Lab 4: Cross-validation and the Bootstrap
10 Classification and Regression trees ISLR Chapter 8.1
11 Bagging and Random Forests ISLR Chapter 8.2.1, 8.2.23
Lab 5: Bagging, Boosting, and Classification and Regression Trees (CART)
12 Boosting ISLR 8.2.3, ESL chapter 10, gbm() vignette
13 Ridge Regression and the LASSO ISLR 6.1, 6.2
14 Distance measures: Euclidean Distance, Manhattan Distance, Mahalanobis Distance, Matching Binary Distance, Asymmetric Binary Distance, Gower’s Distance Ch 3 of MSR4
Lab 6: Ridge Regression and LASSO
15 Hierarchical Clustering and \(k\) - means clustering ISLR 12.4.1, 12.4.2 and 12.4.3
Lab 7: Clustering Techniques
PCA/16 Dimensionality reduction with PCA ILSR 12.2
17 PCA regression and PLS ISLR 6.3.1, 6.3.2
Lab 8:
18 Neural Networks ISLR 10.1, 10.2
19 Gaussian Mixture Models (GMM) (see slides for references)
Review session
Lab Topic
Lab 0 An Introduction to R, R markdown and Quarto.
1 Assessing Regression Models.
2 Regression Diagnostics and Predictive Modeling: Exploring Linear, Polynomial, and KNN Regression
3 Fitting and assessing classification models (e.g. LDA/QDA, logistic regression)
4 Cross-validation and Bootstraping
5 Tree-based methods
6 Ridge Regression/LASSO and PCA
7 Clustering
8 PCAreg, PLS and Neural Nets

Footnotes

  1. You can also open the navigation menu by pressing the M key.↩︎

  2. Note: This feature has only been confirmed to work in Google Chrome and Chromium.↩︎

  3. For more details see Random Forests with R by Robin Genuer, Jean-Michel Poggi (2020)↩︎

  4. Multivariate Statistics with R by Paul J. Hewson↩︎