• Instructor: Nipun Batra (nipun.batra@iitgn.ac.in)
  • Teaching Assistants: Indradeep (indra.mastan@iitgn.ac.in), Sudhakar (sudhakar.kumawat@iitgn.ac.in ), Supratim (supratim.shit@iitgn.ac.in), Shubham (shubam.singh@iitgn.ac.in)
  • Course Timings: Tue and Friday (3:30 to 5 PM in 1/101)
    Office hours: Monday (12 Noon to 1): Please try to stick to this time unless it is an emergency


  • 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]

Grading policy:

  • Project (in groups of 4 or less) (Some ideas from instructors), and some ideas from a Stanford ML course : 40%

  • Project proposal report (due Jan 20) : 5%
  • Phase-I presentation (week of Feb 10) : 5%
  • Phase-II presentation (week of March 15) : 5%
  • Final project 3 minute madness (week of April 20) [See 3MT for inspiration] : 5%
  • Final project demo and poster (week of April 20) : 15%
  • Final report (due April 22) : 5%

  • 3 Surprise quizzes worth 3% each, best 2/3 are recorded for grade : 6%

  • Quiz 1 will be held before or on 15th Jan
  • Quiz 2 will be held before or

  • End semester : 10%
  • Paper presentation [Slides template][3 people argue for each paper. One person summarises the paper, second person acts as an advocate representing the paper; and the third person is the devil’s advocate convincing the jury that paper has flaws] : 4%
  • Kaggle competition : 4% [done individually, due sometime early April]
  • Machine Learning demo (like this or this or this or this) [Same team as project][due 30th March ] : 4%
  • 8 Programming Homework Assignments (50% credit for late submission (upto 1 day for 1st assignment and 2 for others)) [NB - A subset of these will have an associated viva] : 32%
  • Bonus marks - 10 marks if you get into Master level on Kaggle or 5 marks if you get into Expert level on Kaggle

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. FAST.ai course on ML
  5. Practical deep learning for coders by FAST.ai
  6. Course by Alex Ihler, UCI