EE698R: Advanced Topics in Machine Learning (Spring 2022)

Units: 3-0-0-0-9 (3 hours lecture; total 9 credits)
Class timings: MW 17:15-18:30
Instructor: Vipul Arora

TAs

Name	Email
Sumit Kumar	krsumit@iitk.ac.in
Adhiraj Banerjee	adhiraj@iitk.ac.in
Rahul Kodag	rkodag@iitk.ac.in
Arkaprava Biswas	arkapravab20@iitk.ac.in
Ali Faraz	alifaraz@iitk.ac.in
Suraj Kalakoti	kalakoti20@iitk.ac.in
Akanksha Singh	akankss20@iitk.ac.in
Roshan kumar	kroshan20@iitk.ac.in

Registration Note:

• For auditing the course, please email krsumit@iitk.ac.in

Course Objectives:

This course aims at introducing the students to advanced topics in machine learning (ML). The course will begin with lessons on programming which is needed to enable one to efficiently implement ML algorithms. The lectures will focus on mathematical principles, and there will be coding based assignments for implementation.

Last year’s content: https://youtube.com/playlist?list=PLbtAaXHMto-sSss6hS5cxsApQ1dCtfaLb

Pre-requisites:

• Basic course on machine learning (EE698V, EE603A or equivalent). (Lecture videos)[https://www.youtube.com/playlist?list=PLbtAaXHMto-sQHH1qrYn8_D9Fze_D1KhE]
• Digital signal processing (EE301A or equivalent)
• Basics of Programming (ESc101 or equivalent)

The course will need a strong background in linear algebra and probability theory.

Topics:

• Deep Neural Networks
• Generative Modeling
  • Random sampling (Monte Carlo methods)
  • Variational Auto Encoders
  • Generative Adversarial Networks
  • Normalizing Flows
• Time Series Analysis
  • Dynamic Programming
  • Hidden Markov Models
  • Recurrent Neural Networks and LSTMs
    • Connectionist Temporal Classification
• Other topics of interest

Grading Scheme

• Course Notes - 5%
  
• Coding Quiz 1 - 10%
  
• Mid-semester Exam – 30%
  
• Project or Coding Quizzes – 25%
  
• Class participation in presentations - 5% (bonus)
  
• End-semester Exam – 30%
  

Plagiarism Penalty:

As heavy as possible. Zero-tolerance policy.

References:

This course will take excerpts from some standard books on machine learning and signal processing. But it will largely be based on articles and research papers in ML and SP conferences (e.g., NeurIPS, ICML, Interspeech, ICASSP, etc.) and journals (e.g., IEEE TASLP, JMLR, IEEE PAMI, etc.).

Books:

• “Pattern Recognition and Machine Learning”, C.M. Bishop, 2nd Edition, Springer, 2011. https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
• “Bayesian reasoning and machine learning”, D. Barber, Cambridge University Press, 2012. http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf
• “Deep Learning”, I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016. https://www.deeplearningbook.org/