Statistical Learning Theory

Fall 2020

**E-mail:** prakash@cs.mcgill.ca

- I have posted notes below for the lecture on VC dimension on the 16th of September.
- I gave the lecture live on the 14th of September and it was recorded successfully. I have posted some notes below but everything in my notes is also in the book.
- The lecture by Prof. Oberman is recorded and is available from
myCourses. To see it look under
**Content**and**not**under lecture recordings. - Assignment 1 has been posted both here (see below) and on myCourses.
To find it in myCourses look under
**Content**and**not**under Assignments. The Assignments tab on myCourses is for submission of your solutions to assignment 1.**Assignments can be submitted until 11:00 pm.** - The course will be taught
**on campus**in Trottier 100 from 11:35 to 12:55 Mondays and Wednesdays. The lectures**will be recorded**. - This course is co-taught with Prof. Adam Oberman of the Department of Mathematics and Statistics and is cross listed as MATH 597.
- Here is the lecture schedule.
- Here is a course outline.
- The recommended textbooks for the course are
*Understanding Machine Learning*by Shai Shalev-Schwartz and Shai Ben-David and*Foundations of Machine Learning*by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar; this book is available at Mohri's website. These books have not been ordered through the bookstore.

- Lecture Times: MW 11:35 - 12:55
- Lecture Place: Trottier 100
- Office Hours: MW 1:30 - 2:30 by Zoom
- Office: McConnell (North Wing) 105N
- TA and office hours:
- Vincent Luczkow Wed 2:30 - 3:30 by Zoom

- Basic probability notes from 2nd September.
- Notes on basics of PAC-learning.
- Notes on learning rectangles. For the 14th September lecture.
- Notes on learning bounds for the non-realizable case.
- Notes on learning bounds for the agnostic case.
- Notes on VC dimension from 16th September.
- Notes on the learning bound based on VC dimension from 21st September.
- Notes on Rademacher complexity.
- A short note explaining why swapping does not change the distribution of samples.
- October 14th lecture notes on online learning.
- October 19th lecture notes on the Perceptron algorithm.

- Assignment 1. Due on 18th September via myCourses. LaTeX is required.
- Assignment 1 solutions.
- Assignment 2. Due on 2nd October via myCourses.
- Assignment 2 solutions.
- Assignment 4. Due on 4th November at 11pm.

McGill University values academic integrity. Therefore all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures (see http://www.mcgill.ca/integrity for more information). Most importantly, work submitted for this course must represent your own efforts. Copying assignments or tests from any source, completely or partially, or allowing others to copy your work, will not be tolerated.

Every student has the right to submit written work that is to be graded, in English or in French.

Chaque étudiant a le droit de soumettre en français ou en anglais tout travail écrit.