Course Schedule

Class Date Day Topic Reading (Pre-Class) Notes
1 Jan 17 Wed Introduction Pre-Req material; R and Rstudio
Modern Data Science with R sections: 2,3,4,6,7,9		 HW 0 assigned; Get textbook(s); update software; review pre-req material
2 Jan 22 Mon R and Tidyverse R Tidyverse 1-11, 12-14, 16, 20, 25-26, 28
3 Jan 24 Wed Linear Regression with R ISL 3.1-3.6 R formula interface
4 Jan 29 Mon Supervised Learning I ISL 1, 2.1, 7.1
ESL 1, 2.1-2.4		 HW 0 Due
5 Jan 31 Wed Supervised Learning II ISL 2.2-2.3
ESL 2.5-2.9
6 Feb 05 Mon 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
7 Feb 07 Wed Resampling: Cross-Validation and Model Selection ISL 5.1
ESL 7.10
ISL Chap 3 (review)
8 Feb 12 Mon Penalized Regression ISL 6.1-6.2
ESL 3.3-3.4		 HW 2 Due
9 Feb 14 Wed Penalized Regression ISL 6.4-6.5 (optional 6.3)
ESL 3.5-3.6
10 Feb 19 Mon Classification: Probability Modeling ISL 4.1-4.3
ESL 4.1-4.4		 HW 3 Due
11 Feb 21 Wed Classification: Decision Theory ESL 9.1
12 Feb 26 Mon Support Vector Machines (SVM) ISL 9.1-9.6
ESL 12.1-12.3 or MMDS 12.3		 HW 4 Due
13 Feb 28 Wed Prediction Bias and Calibration Calibration: the Achilles heel of predictive analytics
Mar 04 Mon No Class
Mar 06 Wed No Class
14 Mar 11 Mon Tree-Based Methods ISL 8.1, 8.3.1-8.3.2
ESL 9.2		 HW 5 Due
15 Mar 13 Wed Tree-Based Methods ISL 8.2, 8.3.3-8.3.4
ESL 15
16 Mar 18 Mon Ensembles ISL 8.2 HW 6 Due
17 Mar 20 Wed Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
18 Mar 25 Mon Boosting ESL 10.10-10.14
XGBoost paper, CatBoost paper, LightGBM		 Taylor Expansion
19 Mar 27 Wed Review
20 Apr 01 Mon Feature Importance and XAI Interpretable Machine Learning: Permutation Feature Importance HW 7 Due
21 Apr 03 Wed Feature Engineering ISLR 6.3
22 Apr 08 Mon Density Estimation IPSUR: pg. 209-218 (MLE intro)
Maximum Likelihood Estimation: pg. 221-223
IPSUR Chap. 5-7 (Random Variable Review, if necessary)		 HW 8 Due
23 Apr 10 Wed Density Estimation Silverman 2.1-2.6
24 Apr 15 Mon Generative Classifiers ISL 4.4-4.5; ESL 6.6 HW 9 Due
25 Apr 17 Wed Clustering 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
26 Apr 22 Mon Clustering Primary: ESL 8.5.1
Secondary: Gaussian Mixture Models: 11.1-11.3
27 Apr 24 Wed TBD
28 Apr 29 Mon TBD HW 10 Due
May 07 Tue (2pm-5pm) Final Presentations
May 08 Wed (midnight) Final Project Due