Course Schedule

Class Date Day Topic Reading (Pre-Class) Notes
1 Jan 19 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); install software; review pre-req material
2 Jan 24 Mon R and Tidyverse R Tidyverse
3-8, 10-11, 12.3, 12.5, 15, 17-19, 20.1-20.4, 21.1-21.5, 23.4, 27-29
3 Jan 26 Wed Linear Regression with R ISL 3.1-3.6 advertising.Rmd
R formula interface
4 Jan 31 Mon Supervised Learning I ISL 1, 2.1, 7.1
ESL 1, 2.1-2.4
HW 0 Completed
supervised_1.R
5 Feb 02 Wed Supervised Learning II ISL 2.2-2.3
ESL 2.5-2.9
supervised_2.R
6 Feb 07 Mon Resampling: Bootstrap and Splines ISL 5.2, 7.2-7.4
ESL 7.11, 5.1-5.3
HW 1 Due
bootstrap.R
7 Feb 09 Wed Resampling: Cross-Validation and Model Selection ISL 5.1
ESL 7.10
ISL Chap 3 (review)
(R code at end of lecture notes)
8 Feb 14 Mon Penalized Regression ISL 6.1-6.2
ESL 3.3-3.4
HW 2 Due
penalized.R
9 Feb 16 Wed Penalized Regression ISL 6.4-6.5 (optional 6.3)
ESL 3.5-3.6
lasso.R
10 Feb 21 Mon Classification ISL 4.1-4.3
ESL 4.1-4.4
HW 3 Due
classification.R
11 Feb 23 Wed Classification ESL 6.6.3
12 Feb 28 Mon Review HW 4 Due
13 Mar 02 Wed Support Vector Machines (SVM) ISL 9.1-9.6
ESL 12.1-12.3 or MMDS 12.3
svm.R
Mar 07 Mon No Class
Mar 09 Wed No Class
14 Mar 14 Mon Density Estimation IPSUR: pg. 209-218 (MLE intro)
Maximum Likelihood Estimation: pg. 221-223
IPSUR Chap. 5-7 (Random Variable Review, if necessary)
density.R
15 Mar 16 Wed Density Estimation Silverman 2.1-2.6
16 Mar 21 Mon Generative Classifiers ISL 4.4-4.5 HW 5 Due
17 Mar 23 Wed Tree-Based Methods ISL 8.1, 8.3.1-8.3.2
ESL 9.2
trees.R
18 Mar 28 Mon Tree-Based Methods ISL 8.2, 8.3.3-8.3.4
ESL 15
Project Part I Due
19 Mar 30 Wed Ensembles ISL 8.2
20 Apr 04 Mon Review HW 6 Due
21 Apr 06 Wed Boosting ISL 8.2.3; ESL 10.1-10.9
LogitBoost paper
22 Apr 11 Mon Boosting ESL 10.10-10.14
XGBoost paper, CatBoost paper, LightGBM
L2boosting.R
Taylor Expansion
23 Apr 13 Wed Feature Engineering ISLR 6.3
24 Apr 18 Mon 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 7 Due
clustering.R
25 Apr 20 Wed Clustering Primary: ESL 8.5.1
Secondary: Gaussian Mixture Models: 11.1-11.3
26 Apr 25 Mon Association Analysis MMDS 6.1; ITDM 5.1-5.2, 5.7
Instacart Case
HW 8 Due
instacart.R
arules R package
27 Apr 27 Wed Association Analysis ESL 14.2; MMDS 6.2; ITDM 5.3,5.8
28 May 02 Mon Review HW 9 Due
May 10 Tue (10pm) Final Project Due