Program Overview

Program Overview

The CS 725: Foundations of Machine Learning program is offered in the Autumn 2023 semester.

Lecture Schedule

The lecture schedule is as follows:

• Slot 5, Wednesdays, Fridays: 9:30--10:55 am
• Venue: LA 002
• Instructor: Sunita Sarawagi
• Teaching Assistants:
  • Lokesh N
  • Vishak Prasad C
  • Ashutosh Sathe
  • Krishnakant Bhatt
  • Meet Doshi
  • Shrey Bavishi
  • Gurpreet Singh

Prerequisites

The prerequisites for the program include:

• An upper-level undergraduate course(s) in algorithms and data structures
• A basic course on probability and statistics
• Basic understanding of linear algebra and multivariate calculus
• Programming in Python

Eligibility

The course is open only to CS and CMInDS Masters and PhD students. PG students of other departments can write for permission if they meet the necessary prerequisites. UG students are not permitted.

Credit/Audit Requirements

The credit structure is as follows:

• 25% Mid-semester exam
• 35% End semester exam
• 12% Three programming homeworks (Before midsems)
• 15% Class project (After midsems)
• 10% In-class quizzes (best n-2 of n quizzes used for grading)
• 3% Scribe class notes Audit students will get a pass if they attend at least 90% of the lectures and get at least 70% marks in the in-class online quizzes.

Reading Material

The weekwise course calendar and class notes will be made available. Reference books include:

• "Probabilistic Machine Learning" by Kevin Murphy
• "Understanding Machine Learning" by Shai Shalev-Shwartz and Shai Ben-David
• "Pattern recognition and machine learning" by Christopher Bishop
• "The elements of Statistical Learning" by Hastie, Tibshirani, Friedman
• "Dive into Deep Learning" by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola Supplementary books include:
• "Probability, Random Variables and Stochastic processes" by Papoulis and Pillai
• "Convex optimization" by Boyd and Vandenberghe
• "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• "Linear Algebra and Its Applications" by Gilbert Strand

Other Useful Resources

Other useful resources include:

• Andrew Ng's offering on Coursera
• Lecture notes from some of the previous offerings of the class
• Demo applets for various machine learning operations