This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses, with all changes documented here.
lecture
dow
date
week
topic
prepare
slides
hw
project
job
hack-a-thon
notes
0
M
Feb 3
1
Welcome
1
W
Feb 5
1
Big Picture
HW 1 Assigned
2
F
Feb 7
1
What is Statistical Learning
3
M
Feb 10
2
Intro to Linear Regression and Gradient Descent
4
W
Feb 12
2
Multiple Linear Regression and Data Splitting
HW 1 Due/ HW 2 Assigned
5
F
Feb 14
2
Data Splitting and KNN
6
M
Feb 17
3
KNN + Preprocessing
7
W
Feb 19
3
Classification and Logistic Regression
HW 3 Assigned
8
F
Feb 21
3
Classification Metrics
9
M
Feb 24
4
Job Application Intro
HW 2 Due / Job Application 1 Assigned
10
W
Feb 26
4
ROC and AUC
11
F
Feb 28
4
Cross-Validation
12
M
Mar 3
5
Pre-processing, Missing Data, and CV
HW 3 Due/ HW 4 Assigned
13
W
Mar 5
5
Pre-processing, Missing Data, and CV Continued
14
F
Mar 7
5
Resume/Cover Letter Peer-Review
Cover Letter and Resume Due in Class (Attendance Mandatory)
15
M
Mar 10
6
Workflow Sets and Feature Selection
HW 4 Due/ HW 5 Assigned
16
W
Mar 12
6
Feature Selection and Regularization
17
F
Mar 14
6
Regularization
18
M
Mar 17
7
Model Tuning
HW 5 Due / HW 6 Assigned
19
W
Mar 19
7
Regression Trees
20
F
Mar 21
7
Regression Trees Continued
Job Application 1 Due
M
NA
Spring Break
W
NA
Spring Break
F
NA
Spring Break
21
M
Mar 31
8
Job Interview Intro, Decision Trees Continued
HW 6 Due / HW 7 Assigned / Job Interviews Announced / Project Announced
22
W
Apr 2
8
Classification Trees
23
F
Apr 4
8
Brian Bava Visit
Questions for Brian Due
24
M
Apr 7
9
Classification Trees Continued
HW 7 Due/ HW 8 Assigned
25
W
Apr 9
9
Bagging/Random Forests
26
F
Apr 11
9
Job Interview Panel
27
M
Apr 14
10
Boosting
HW 8 Due/ HW 9 Assigned
28
W
Apr 16
10
More on Classification
29
F
Apr 18
10
More on Classification
30
M
Apr 21
11
Imbalanced Classes
HW 9 Due/ HW 10 Assigned
31
W
Apr 23
11
High Performance Computing (Guest Lecturer Jim Beck)