ES 654 Machine Learning


  • Instructor: Nipun Batra (
  • Teaching Assistants: Zeel B Patel, Shriraj Sawant, Shrutimoy, Sarth Dubey, Hitarth Gandhi, Saagar Parikh, Eshan Gujarathi, Gautam Vashishta, Aadesh Desai
  • Course Timings: Tuesday, Thursday 330-450 PM IST in 1/101
  • Slack Invite


  • Good experience in Python programming
  • Probability
  • Linear Algebra

Course preparation: Students are encouraged to study some of the following to refresh their understanding of some of the prerequisities before the course formally begins.

Reference textbooks:

  1. Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R
  2. Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006.[Freely available online]
  3. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York, NY, USA:: Springer series in statistics; 2001.[Freely available online]
  4. Duda RO, Hart PE, Stork DG. Pattern classification. John Wiley & Sons; 2012 Nov 9.
  5. Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 1997;45(37):870-7.
  6. Murphy, K. Machine Learning: A Probabilistic Perspective. MIT Press
  7. Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18.[Freely available online]

Some other ML courses

  1. NPTEL course by Balaram Ravindran
  2. CMU course by Tom Mitchell and Maria-Florina Balcan
  3. Coursera ML course by Andrew Ng
  4. course on ML
  5. Practical deep learning for coders by
  6. Course by Alex Ihler, UCI