E2 236, Spring 2022

Foundations of Machine Learning

Lectures

Homework

Programming Assignments Due Dates

Tests

  • 19 Feb 2022: Mid-term-01
  • 19 Mar 2022: Mid-term-02
  • 27 Apr 2022: Final (02:00pm-05:00pm, EC 1.07)

Grading Policy

Homeworks : 10
Assignments: 30
Midterms : 30
Final : 30
Attendance : 10

Course Syllabus

  • Support Vector Machines, Kernel methods
  • PAC learning framework, learning via uniform convergence
  • Bias complexity trade-off, Rademacher complexity, VC-dimension
  • Multiclass classification, decision trees, nearest neighbors
  • Parameter estimation and nonparametric regression
  • Convex optimization, Stochastic gradient descent
  • Neural Networks, Deep learning
  • Reinforcement learning and deep reinforcement learning
  • Online learning
  • Dim reduction, feature representation/extraction, clustering.

Course Description

This course provides performance guarantees on various classes of machine learning algorithms.

Teams/GitHub Information

Teams

We will use Microsoft Teams for all the course related communication.
Please do not send any email regarding the course.
You can signup for the course team Foundations-Machine-Learning-2022 using the following code q8qd4up.
To be on the course team, you have to be formally registered for the course.
If you are registered for the course and not on the course team Foundations-Machine-Learning-2022, please send me a direct message on Teams.

GitHub

Students can signup for GitHub.
All the students in the class have read access to GitHub public repository Machine-Learning.
For the write access to this GitHub repository, please send me your github userid on the course slack channel.
GitHub would be used for scribing lecture notes and submitting programming assignments.
Please follow the guidelines in the sample lecture.
The source file for the sample lecture is in the repository.
It is recommended you save it with another name in your local repository for creating a new lecture.
Here is a good book for Git and a simple tutorial.

Instructors

Vinod Sharma
Office: EC 2.07
Hours: By appointment.

Parimal Parag
Office: EC 2.17
Hours: By appointment.

Time and Location

Classroom: EC 1.08, ECE main Building
Hours: Tu-Th 03:30-05:00pm.
Tutorial/Quiz: Fri 07:50am.

Teaching Assistant

Amrit Priyadarshi
Email: amritp@iisc.ac.in

Ayush Kumar Gupta
Email: ayushkg@iisc.ac.in

Nakul Vijay Raichur
Email: nakulvijay@iisc.ac.in

Hours: By appointment.

Textbooks

Foundations of machine learning, Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.

Understanding machine learning, Shai Shalev-Shwartz and Shai Ben-David.