Detailed Syllabus


Current quarter's videos are available through Canvas.

LectureDate    Lecture TopicsReadingsCoursework
1 Mon
Jun 26
Introduction and Background
Supervised vs Unsupervised learning (slides)
ISL Ch 1-2
ESL Ch 1-2
HW 0 (No due date)
HW 1 - data (due Jul 7)
2 Wed
Jun 28
Classification & Clustering (slides) ISL Ch 2.2.3, 12.4
ESL Ch 6.6.3
Section Fri
Jun 30
Probability, Statistics, and Linear Algebra review (slides)
3 Mon
Jul 3
Linear Regression (slides) ISL Ch 3
ESL Ch 3
4 Wed
Jul 5
Finish Linear Regression & Classification (slides) ISL Ch 3-4
ESL Ch 3
Section Fri
Jul 7
R and Python Programming (R basics, Python basics) HW 1 due @ 4:30PM
HW 2 - data (due Jul 17)
5 Mon
Jul 10
Evaluation and Training (slides) ISL Ch 5.1
ESL Ch 7, 8.8
6 Wed
Jul 12
Estimating uncertainty (slides) ISL Ch 5
ESL Ch 7
Section Fri
Jul 14
No class
7 Mon
Jul 17
Model Selection & Regularization (slides) ISL Ch 6
ESL Ch 18
HW 2 due @ 4:30PM
Wed
Jul 19
MIDTERM (practice exam)
Time: 4:30 PM to 5:50 PM - Location: Skilling.
Section Fri
Jul 21
No class
8 Mon
Jul 24
Beyond linearity (slides) ISL Ch 6.4-7
ESL Ch 5
HW 3 - data (due Aug 2)
9 Wed
Jul 26
Support Vector Machines (slides) ISL Ch 9
ESL Ch 12.1-12.3
Section Fri
Jul 28
No class
10 Mon
Jul 31
Decision Trees & Random Forests (slides) ISL Ch 8
ESL Ch 9.2,15
11 Wed
Aug 2
Boosting (slides) ISL Ch 8.2
ESL Ch 10
HW 3 due @ 4:30PM
HW 4 - data (due Aug 11)
Section Fri
Aug 4
Final project review 11
12 Mon
Aug 7
Neural Networks (slides) ISL Ch 10
ESL Ch 11
13 Wed
Aug 9
Survival Analysis (slides) ISL Ch 11
Section Fri
Aug 11
Final exam review (slides) ESL Ch 11 HW 4 due @ 4:30PM
Optional:
Final report submission
14 Mon
Aug 14
Final project discussion Final project (predictions) due @ 12:00AM
Wed
Aug 16
No class Final project (report) due @ 11:59PM
Sat
Aug 19
FINAL EXAM
Time: 7:00 PM - 10:00 PM - Location: Skilling Auditorium.

Additional Reading: Surveys, Tutorials, etc.


R programming
  1. An Introduction to R (CRAN)
  2. An Introduction to R (Douglas et al)
  3. R for Data Science
  4. Advanced R
  5. Quick R
Python programming
  1. Python For Beginners
  2. A Practical Introduction to Python Programming
  3. A Complete Python Tutorial to Learn Data Science from Scratch
  4. Python for Data Analysis
  5. Introduction to Python for Econometrics, Statistics and Data Analysis
Online lectures
  1. Introduction to Statistical Learning (Trevor Hastie, Robert Tibshirani)
  2. Machine Learning (Andrew Ng)
  3. Mining Massive Datasets (various lecturers)
  4. Statistical Learning Theory and Applications (Lorenzo Rosasco)