Course Schedule

Class Date Day Topic Reading (Pre-Class) Notes
1 Jan 13 Mon Introduction Pre-Req material; R and Rstudio
Modern Data Science with R sections: 2,3,4,6,7,9		 HW 0 assigned; Download textbooks; update software; review pre-req material
2 Jan 15 Wed Linear Regression with R ISL 3.1-3.6 R formula interface
Jan 16 Thu HW 0 Due
Jan 20 Mon No Class
3 Jan 22 Wed Supervised Learning I ISL 1, 2.1, 7.1
ESL 1, 2.1-2.4
4 Jan 27 Mon Supervised Learning II ISL 2.2-2.3
ESL 2.5-2.9
5 Jan 29 Wed Resampling: Bootstrap and Splines ISL 5.2, 7.2-7.4
ESL 7.11, 5.1-5.3
(optional) Bootstrapping Regression Models
Jan 30 Thu HW 1 Due
6 Feb 03 Mon Resampling: Cross-Validation and Model Selection ISL 5.1
ESL 7.10
ISL Chap 3 (review)
7 Feb 05 Wed Penalized Regression ISL 6.1-6.2
ESL 3.3-3.4
Feb 06 Thu HW 2 Due
8 Feb 10 Mon Penalized Regression ISL 6.4-6.5 (optional 6.3)
ESL 3.5-3.6
9 Feb 12 Wed Tree-Based Methods ISL 8.1, 8.3.1-8.3.2
ESL 9.2
Feb 13 Thu HW 3 Due
10 Feb 17 Mon Tree-Based Methods ISL 8.2, 8.3.3-8.3.4
ESL 15
11 Feb 19 Wed Classification: Probability Modeling ISL 4.1-4.3
ESL 4.1-4.4
Feb 20 Thu HW 4 Due
12 Feb 24 Mon Classification: Decision Theory ESL 9.1
13 Feb 26 Wed Support Vector Machines (SVM) ISL 9.1-9.6
ESL 12.1-12.3 or MMDS 12.3
Feb 27 Thu HW 5 Due
14 Mar 03 Mon Prediction Bias and Calibration Calibration: the Achilles heel of predictive analytics
15 Mar 05 Wed Review
Mar 06 Thu HW 6 Due
Mar 10 Mon No Class
Mar 12 Wed No Class
16 Mar 17 Mon No Class
17 Mar 19 Wed Ensembles and Stacking ISL 8.2
18 Mar 24 Mon Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
19 Mar 26 Wed Boosting ESL 10.10-10.14
XGBoost, CatBoost, LightGBM
Mar 27 Thu HW 7 Due
20 Mar 31 Mon Special topics in boosting Generalized Additive Models (GAM)
21 Apr 02 Wed Feature Importance and XAI Interpretable Machine Learning: Permutation Feature Importance
Apr 03 Thu HW 8 Due
22 Apr 07 Mon Feature Engineering ISLR 6.3
23 Apr 09 Wed Density/Probability Estimation IPSUR: pg. 209-218 (MLE intro)
Maximum Likelihood Estimation: pg. 221-223
IPSUR Chap. 5-7 (Random Variable Review, if necessary)
Apr 10 Thu HW 9 Due
24 Apr 14 Mon Density/Probability Estimation Silverman 2.1-2.6
25 Apr 16 Wed Generative Classifiers ISL 4.4-4.5; ESL 6.6
Apr 17 Thu HW 10 Due
26 Apr 21 Mon Clustering 1 Primary: ISL 12.4-12.5; ITDM 7.1; 7.5
Secondary: ESL 14.3.1-14.3.8; 14.3.12; ITDM 7.2-7.3
27 Apr 23 Wed Clustering 2 Primary: ESL 8.5.1
Secondary: Gaussian Mixture Models: 11.1-11.3
28 Apr 28 Mon TBD
May 03 Sat Final Exam
Due 5pm