Detailed Syllabus


Current quarter's videos are available through Canvas.

LectureDate    Lecture TopicsReadingsCoursework
1 Mon
Jun 23
Introduction and Background
Supervised vs Unsupervised learning (slides)
ISL Ch 1-2
ESL Ch 1-2
HW 0 (No due date)
HW 1 (Due Jul 3)
2 Wed
Jun 25
Classification & Clustering (slides) ISL Ch 2.2.3, 12.4
ESL Ch 6.6.3
Section Fri
Jun 27
Probability, Statistics, and Linear Algebra review (slides)
3 Mon
Jun 30
Linear Regression (slides) ISL Ch 3
ESL Ch 3
4 Wed
Jul 2
Finish Linear Regression & Classification (slides) ISL Ch 3-4
ESL Ch 3
Section Fri
Jul 4
No class HW 1 due @ 4:30PM
HW 2 (Due Jul 14)
5 Mon
Jul 7
Evaluation and Uncertainty (slides) ISL Ch 5
ESL Ch 7, 8.8
6 Wed
Jul 9
Model Selection & Regularization (slides) ISL Ch 6
ESL Ch 18
Section Fri
Jul 11
Midterm review (slides)
7 Mon
Jul 14
Beyond linearity (slides) ISL Ch 6.4-7
ESL Ch 5
HW 2 due @ 4:30PM
Wed
Jul 16
MIDTERM (practice exam)
Time: 4:30 PM to 5:50 PM - Location: Packard 101.
Section Fri
Jul 18
No class HW 3 (Due Jul 28)
8 Mon
Jul 21
Support Vector Machines (slides) ISL Ch 9
ESL Ch 12.1-12.3
9 Wed
Jul 23
Decision Trees & Random Forests (slides) ISL Ch 8
ESL Ch 9.2,15
Section Fri
Jul 25
No class
10 Mon
Jul 28
Boosting (slides) ISL Ch 8.2
ESL Ch 10
HW 3 due @ 4:30PM
HW 4 (Due Aug 6)
11 Wed
July 30
Neural Networks (slides) ISL Ch 10
ESL Ch 11
Section Fri
Aug 1
Final project review
12 Mon
Aug 4
An Introduction to Transformers (slides) Attention Is All You Need
13 Wed
Aug 6
Survival Analysis (slides) ISL Ch 11 HW 4 due @ 4:30PM
Section Fri
Aug 8
Final exam review (slides) ESL Ch 11 Optional: Final report submission
14 Mon
Aug 11
Final project discussion Final project (predictions) due @ 12:00AM
Wed
Aug 13
No class Final project (report) due @ 11:59PM
Sat
Aug 16
FINAL EXAM
Time: 7:00 PM - 10:00 PM - Location: TBD.

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)