Course Schedule

Class Date Day Topic Reading (Pre-Class) Notes
1 Aug 27 Tue 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 Aug 29 Thu R and Tidyverse R Tidyverse 2-12, 19, 26-27, 29
ISL 3.1-3.6		 R formula interface; Linear Regression with R
3 Sep 03 Tue Supervised Learning I ISL 1, 2.1, 7.1
ESL 1, 2.1-2.4		 HW 0 Due
4 Sep 05 Thu Supervised Learning II ISL 2.2-2.3
ESL 2.5-2.9
5 Sep 10 Tue Resampling: Bootstrap and Splines ISL 5.2, 7.2-7.4
ESL 7.11, 5.1-5.3
(optional) Bootstrapping Regression Models		 HW 1 Due
6 Sep 12 Thu Resampling: Cross-Validation and Model Selection ISL 5.1
ESL 7.10
ISL Chap 3 (review)
7 Sep 17 Tue Penalized Regression ISL 6.1-6.2
ESL 3.3-3.4		 HW 2 Due
8 Sep 19 Thu Penalized Regression ISL 6.4-6.5 (optional 6.3)
ESL 3.5-3.6
9 Sep 24 Tue Tree-Based Methods ISL 8.1, 8.3.1-8.3.2
ESL 9.2		 HW 3 Due
10 Sep 26 Thu Tree-Based Methods ISL 8.2, 8.3.3-8.3.4
ESL 15
11 Oct 01 Tue Classification: Probability Modeling ISL 4.1-4.3
ESL 4.1-4.4		 HW 4 Due
12 Oct 03 Thu Classification: Decision Theory ESL 9.1
13 Oct 08 Tue Support Vector Machines (SVM) ISL 9.1-9.6
ESL 12.1-12.3 or MMDS 12.3		 HW 5 Due
14 Oct 10 Thu Prediction Bias and Calibration Calibration: the Achilles heel of predictive analytics
Oct 15 Tue No Class
15 Oct 17 Thu Review #1 HW 6 Due (Oct 18)
16 Oct 22 Tue Ensembles and Stacking ISL 8.2
17 Oct 24 Thu Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
18 Oct 29 Tue Boosting ESL 10.10-10.14
XGBoost, CatBoost, LightGBM		 HW 7 Due
Taylor Expansion
19 Oct 31 Thu GAM and Stumps Generalized Additive Models (GAM)
Nov 05 Tue No Class
20 Nov 07 Thu Feature Importance and XAI Interpretable Machine Learning: Permutation Feature Importance HW 8 Due (Nov 8)
21 Nov 12 Tue Feature Engineering ISLR 6.3
22 Nov 14 Thu Density Estimation IPSUR: pg. 209-218 (MLE intro)
Maximum Likelihood Estimation: pg. 221-223
IPSUR Chap. 5-7 (Random Variable Review, if necessary)		 HW 9 Due
23 Nov 19 Tue Density/Probability Estimation Silverman 2.1-2.6
24 Nov 21 Thu Generative Classifiers ISL 4.4-4.5; ESL 6.6
25 Nov 26 Tue No Class HW 10 Due
Nov 28 Thu No Class
26 Dec 03 Tue 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 Dec 05 Thu Clustering 2 Primary: ESL 8.5.1
Secondary: Gaussian Mixture Models: 11.1-11.3
Dec 12 Thu (8am) Final Exam Due