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 #1
Mar 06 Thu HW 6 Due
Mar 10 Mon No Class
Mar 12 Wed No Class
16 Mar 17 Mon Ensembles and Stacking ISL 8.2
17 Mar 19 Wed Mid-term exam
18 Mar 24 Mon Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
19 Mar 26 Wed Taylor Expansion 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
May 05 Mon Final Presentations
9:30am - 10:45am
May 05 Mon Final Report
5pm
28 TBD