Course Schedule

Class Date Day Topic Reading (Pre-Class) Notes
1 Aug 22 Tue 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 Aug 24 Thu R and Tidyverse;
Linear Regression with R		 R Tidyverse 2-12, 19, 26-27, 29
ISL 3.1-3.6		 R formula interface
3 Aug 29 Tue Supervised Learning I ISL 1, 2.1, 7.1
ESL 1, 2.1-2.4		 HW 0 Due
4 Aug 31 Thu Supervised Learning II ISL 2.2-2.3
ESL 2.5-2.9
5 Sep 05 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 07 Thu Resampling: Cross-Validation and Model Selection ISL 5.1
ESL 7.10
ISL Chap 3 (review)
7 Sep 12 Tue Penalized Regression ISL 6.1-6.2
ESL 3.3-3.4		 HW 2 Due
8 Sep 14 Thu Penalized Regression ISL 6.4-6.5 (optional 6.3)
ESL 3.5-3.6
9 Sep 19 Tue Probability Modeling ISL 4.1-4.3
ESL 4.1-4.4		 HW 3 Due
10 Sep 21 Thu Classification ESL 9.1
11 Sep 26 Tue Support Vector Machines (SVM) ISL 9.1-9.6
ESL 12.1-12.3 or MMDS 12.3		 HW 4 Due
12 Sep 28 Thu Prediction Bias and Calibration Calibration: the Achilles heel of predictive analytics
Oct 03 Tue No Class
13 Oct 05 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 5 Due
14 Oct 10 Tue Density Estimation Silverman 2.1-2.6
15 Oct 12 Thu Generative Classifiers ISL 4.4-4.5; ESL 6.6
16 Oct 17 Tue 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		 HW 6 Due
17 Oct 19 Thu Clustering Primary: ESL 8.5.1
Secondary: Gaussian Mixture Models: 11.1-11.3
18 Oct 24 Tue Tree-Based Methods ISL 8.1, 8.3.1-8.3.2
ESL 9.2		 HW 7 Due
19 Oct 26 Thu Tree-Based Methods ISL 8.2, 8.3.3-8.3.4
ESL 15
20 Oct 31 Tue Ensembles ISL 8.2 HW 8 Due
21 Nov 02 Thu Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
Nov 07 Tue No Class
22 Nov 09 Thu Boosting ESL 10.10-10.14
XGBoost paper, CatBoost paper, LightGBM		 Taylor Expansion
23 Nov 14 Tue Review HW 9 Due
24 Nov 16 Thu Feature Engineering ISLR 6.3
25 Nov 21 Tue Feature Importance Interpretable Machine Learning: Permutation Feature Importance
Nov 23 Thu No Class
26 Nov 28 Tue Time Series and Forecasting (Part 1) Forecasting Principles and Practice Chapters 2-3
Facebook Prophet		 HW 10 Due
27 Nov 30 Thu Time Series and Forecasting (Part 2) Forecasting Principles and Practice Chapters 5.10, 8
28 Dec 05 Tue TBD
Dec 12 Tue (8am) Final Project Due