EE698R: Advanced Topics in Machine Learning (Spring 2021)

Vipul Arora
Department of Electrical Engineering, IIT Kanpur

Course Objectives:

This course aims at introducing the students to advanced topics in machine learning (ML). The course will begin with lessons on programming that 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.

Registration: (Updated on 23 Nov, 10AM)

No more space for UG students. But I am accepting all students who have done or are doing projects with me.
All PG students will be accepted and are encouraged to apply.

For auditing the course, wait until the start of next sem. Request the TAs to add you to mooKIT then.

Pre-requisites:

• Basic course on machine learning (EE698V or equivalent)
• 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:

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

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/