| Class | Date | Day | Topic | Notes | Background & Reference |
|---|---|---|---|---|---|
| 1 | Jan 13 | Tue | Introduction | update software review pre-req material R practice |
Modern Data Science with R sections: 2,3,4,6,7,9 |
| 2 | Jan 15 | Thu | Prediction Pipeline | R tidymodels Python Pipeline |
|
| 3 | Jan 20 | Tue | Supervised Learning I | ISL 1, 2.1, 7.1 ESL 1, 2.1-2.4 |
|
| Jan 21 | Wed | HW 0 Due | Homework #0 (.qmd) | ||
| 4 | Jan 22 | Thu | Supervised Learning II | Quiz | ISL 2.2-2.3 ESL 2.5-2.9 |
| 5 | Jan 27 | Tue | Model Selection (I) | ISL 5.1 ESL 7.10 ISL Chap 3 (review) |
|
| Jan 28 | Wed | HW 1 Due | Homework #1 (.qmd) | ||
| 6 | Jan 29 | Thu | Model Selection (II) | Quiz | |
| 7 | Feb 3 | Tue | Prediction Trees | ISL 8.1, 8.3.1-8.3.2 ESL 9.2 |
|
| Feb 4 | Wed | HW 2 Due | Homework #2 (.qmd) | ||
| 8 | Feb 5 | Thu | Random Forest | Quiz | ISL 8.2, 8.3.3-8.3.4 ESL 15 |
| 9 | Feb 10 | Tue | Probability and Classification | ISL 4.1-4.3 ESL 4.1-4.4, 9.1 |
|
| Feb 11 | Wed | HW 3 Due | Homework #3 (.qmd) | ||
| 10 | Feb 12 | Thu | Generative Classifiers | Quiz | ISL 4.4-4.5; ESL 6.6 |
| 11 | Feb 17 | Tue | Support Vector Machines | ISL 9.1-9.6 ESL 12.1-12.3 or MMDS 12.3 |
|
| Feb 18 | Wed | HW 4 Due | Homework #4 (.qmd) | ||
| 12 | Feb 19 | Thu | Calibrating Risk Scores | Quiz | Calibration: the Achilles heel of predictive analytics |
| 13 | Feb 24 | Tue | Review | ||
| 14 | Feb 26 | Thu | Midterm Exam | ||
| Mar 3 | Tue | No Class | |||
| Mar 5 | Thu | No Class | |||
| 15 | Mar 10 | Tue | Ensemble and Stacking | ISL 8.2 | |
| 16 | Mar 12 | Thu | Gradient Boosting | ISL 8.2.3; ESL 10.1-10.9, ESL 10.10-10.14 XGBoost, CatBoost, LightGBM |
|
| 17 | Mar 17 | Tue | Predictive Diagnostics | Calibration: the Achilles heel of predictive analytics | |
| Mar 18 | Wed | HW 5 Due | Homework #5 (.qmd) | ||
| 18 | Mar 19 | Thu | Predictive Inference | Quiz | |
| 19 | Mar 24 | Tue | Sampling I | ||
| Mar 25 | Wed | HW 6 Due | Homework #6 (.qmd) | ||
| 20 | Mar 26 | Thu | Sampling II | Quiz | |
| 21 | Mar 31 | Tue | Forecasting I | ||
| Apr 1 | Wed | HW 7 Due | Homework #7 (.qmd) | ||
| 22 | Apr 2 | Thu | Forecasting II | Quiz | |
| 23 | Apr 7 | Tue | Survival Modeling I | ||
| Apr 8 | Wed | HW 8 Due | Homework #8 (.qmd) | ||
| 24 | Apr 9 | Thu | Survival Modeling II | Quiz | |
| 25 | Apr 14 | Tue | Recommender Systems I | ||
| Apr 15 | Wed | HW 9 Due | Homework #9 (.qmd) | ||
| 26 | Apr 16 | Thu | Recommender Systems II | Quiz | |
| 27 | Apr 21 | Tue | Review | ||
| Apr 22 | Wed | HW 10 Due | Homework #10 (.qmd) | ||
| 28 | Apr 23 | Thu | Review | Quiz | |
| May 4 | Mon | Final Exam (2-5pm) |
Course Schedule
Reading
- ISL: An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani
- ESL: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition) by Hastie, Tibshirani, and Friedman
- ITDM: Introduction to Data Mining (Second Edition) by Tan, Steinbach, Karpatne, and Kumar
- MMDS: Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
- FPP: Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos