Schedule

The schedule below is a tentative outline of our plans for the semester.

Before each class period, please watch the indicated videos and check on your understanding by actively reviewing the associated Learning Goals. The readings listed below are optional but serve as a nice complement to the videos and class activities. Readings refer to chapters/sections in the Introduction to Statistical Learning (ISLR) textbook (available online here).

Remember to take notes on where you paused/rewound/reread or smiled/nodded during the videos/readings. This is essential for the Metacognitive Reflection part of the course.


Week 1: Intros and Evangelizing Evaluation (1/20 - 1/27)
Dates Topic Videos/Readings Video Slides Class Slides
1/20 Introductions ISLR: Ch 1, Ch 2--Section 2.1
(Skip 2.1.2, 2.1.3 for now.)
1/23 Evaluating Regression Models Evaluating Regression Models
R: Introduction to TidyModels
ISLR: 2.2

1/25 Overfitting Overfitting
R: Preprocessing and Recipes
ISLR: 5.1
1/27 Cross-validation Cross-validation
R: Training, Testing and Cross-Validation
ISLR: 5.1

N/A
Start Homework 1 due Friday, 2/3 at midnight CST
Week 2: A Soirée with Selection Strategies (1/30 - 2/3)
Dates Topic Videos/Readings Video Slides Class Slides
1/30 Subset Selection Variable Subset Selection
R: Subset Selection
ISLR: 6.1
2/1 LASSO (Shrinkage/Regularization) LASSO (Shrinkage/Regularization)
R: LASSO and Regularization
ISLR: 6.2
2/3 LASSO (continued)
Finish Homework 1 due Friday, 2/3 at midnight CST
Week 3: A Treasure in Tradeoffs (2/6 - 2/10)
Dates Topic Videos/Readings Video Slides Class Slides
2/6 KNN Regression and the Bias-Variance Tradeoff KNN Regression and the Bias-Variance Tradeoff
R: KNN Regression
ISLR: 2.2.2 (bias-variance tradeoff); 3.5 (KNN regression)
2/8 Quiz 1 (Covers Topics 1 to 6. You may bring a 3x5 notecard with you.)
2/10 Catch-up day: We'll practice deepening our understanding of the bias-variance tradeoff and work towards building a tidymodels reference sheet.
Start Homework 2 due Friday, 2/17 at midnight CST
Week 4: A Feast with Flexibility (2/13 - 2/17)
Dates Topic Videos/Readings Video Slides Class Slides
2/13 Modeling Nonlinearity: Polynomial Regression and Splines Modeling Nonlinearity: Polynomial Regression and Splines
R: Nonlinearity: Polynomial Regression and Splines ISLR: 7.1-7.4
2/15 Review of Quiz 1
2/17 Local Regression and Generalized Additive Models Local Regression and Generalized Additive Models
R: Local Regression and GAMs
ISLR: 7.6-7.7
Finish Homework 2 due Friday, 2/17 at midnight CST
Week 5: Regression Wrap-Up and Commencing Classification (2/20 - 2/24)
Dates Topic Videos/Readings Video Slides Class Slides
2/20 Review and synthesis of our regression unit
2/22 Quiz 2 (Covers bias-variance tradeoff, KNN, local regression, GAMs. You may bring a 3x5 notecard with you.)
2/24 Logistic Regression Logistic Regression
R: Logistic Regression
ISLR: 4.1 - 4.3
Start Homework 3 due Friday, 3/3 at midnight CST (Portfolio + Reflection) and Friday 3/10 at midnight (Project Work)
Week 6: Staying Classy with Classification (2/27 - 3/3)
Dates Topic Videos/Readings Video Slides Class Slides
2/27 Evaluating Classification Models & Revisiting LASSO+KNN Evaluating Classification Models
R: Evaluating Classification
3/1 Evaluating Classification Models & Revisiting LASSO+KNN (continued)
3/3 Capstone Days! (no class - attend a talk instead!)
Finish Homework 3 due Friday, 3/3 at midnight CST (Portfolio + Reflection) and Friday 3/10 at midnight (Project Work)
Week 7: Learning Conferences (3/6 - 3/10)
No class this week to make space for Learning Conferences. Schedule a conference with the instructor this week using the Calendly link under the Moodle course calendar. (Please sign up by 3/3.)
Week 8: Travels with Trees (3/20 - 3/24)
Dates Topic Videos/Readings Video Slides Class Slides
3/22 Decision Trees (Conceptual) Decision Trees
ISLR: 8.1
3/24 Decision Trees (Coding) R: Decision Trees
Finish Homework 4 due Friday, 3/24 at midnight CST
Week 9: Classification Wrap-up(3/27 - 3/31)
Dates Topic Videos/Readings Video Slides Class Slides
3/27 Bagging and Random Forests (Conceptual) Bagging and Random Forests
ISLR: 8.2.1, 8.2.2
3/29 Bagging and Random Forests (Coding) R: Bagging and Random Forests
3/31 Quiz 3 (NOT IN CLASS--ON YOUR OWN) (Covers our classification unit: evaluating classification models, (LASSO) logistic regression, decision trees, bagging and random forests. You may use a 3x5 notecard but no other notes.)
Start Homework 5 due Friday, 4/7 at midnight CST
Week 10: Crazy for Clustering (4/3 - 4/7)
Dates Topic Videos/Readings Video Slides Class Slides
4/3 K-Means Clustering (Conceptual) K-Means Clustering
ISLR: 12.4.1, 12.4.3
4/5 Hierarchical Clustering (Conceptual) Hierarchical Clustering
ISLR: 12.4.2, 12.4.3
4/7 Hierarchical and K-Means Clustering (Coding)
Finish Homework 5 due Friday, 4/7 at midnight CST
Week 11: Dabbling in Dimension Reduction (4/10 - 4/14)
Dates Topic Videos/Readings Video Slides Class Slides
4/10 Principal Components Analysis (Conceptual) Principal Components Analysis
ISLR: 12.2
4/12 Principal Components Analysis (Coding)
4/14 Quiz 4 (NOT IN CLASS--ON YOUR OWN) (Covers our unsupervised learning unit: k-means and hierarchical clustering, principal components analysis. You may use a 3x5 notecard but no other notes.)
Start Homework 6 due Friday, 4/21 at midnight CST
Week 12: Project Work
Dates Topic Videos/Readings Video Slides Class Slides
4/17 Project work day: work on presentations in class
4/19 Project work day: work on presentations in class
4/21 Project work day: peer review of presentations
Finish Homework 6 due Friday, 4/21 at midnight CST, and start working on the Final Metacognitive Reflection due Thursday, 4/27 at midnight CST.
Week 13: Project Work
Dates Topic Videos/Readings Video Slides Class Slides
4/24 No class this week. Use Calendly to sign up for a time to meet with the instructor to present your draft presentation and receive feedback.
4/26 No class this week. Use Calendly to sign up for a time to meet with the instructor to present your draft presentation and receive feedback.
4/28 No class this week. Use Calendly to sign up for a time to meet with the instructor to present your draft presentation and receive feedback.
Finish the Final Metacognitive Reflection due Thursday, 4/27 at midnight CST. Work on Final Project due Friday, 5/5 at midnight CST.
Week 14: Project Work
Dates Topic Videos/Readings Video Slides Class Slides
5/1 Course evaluations and course wrap-up/review
Finish Final Project due Friday, 5/5 at midnight CST