ES 654: Machine Learning [2020]
- Instructor: Nipun Batra (nipun.batra@iitgn.ac.in)
- Teaching Assistants: Indradeep (indra.mastan@iitgn.ac.in, Supratim (supratim.shit@iitgn.ac.in), Pankaj Pandey, Ritik Dutta, Varun Gohil, Abhinav Narayan Harish, Shreyas Singh
- Course Timings: 5 PM to 6:30 PM on Monday and Wednesday (1/102)
- Office hours: Monday (12 Noon to 1): Please try to stick to this time unless it is an emergency
- Course Calendar and Course FAQs
- Pre-requisites:
- 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.
- First four chapters of the Python Data Science handbook
- Some material on Linear Algebra
- Khan academy course on Stats and Probability
Reference textbooks:
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R
- Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006.[Freely available online]
- Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York, NY, USA:: Springer series in statistics; 2001.[Freely available online]
- Duda RO, Hart PE, Stork DG. Pattern classification. John Wiley & Sons; 2012 Nov 9.
- Mitchell TM. Machine learning. 1997. Burr Ridge, IL: McGraw Hill. 1997;45(37):870-7.
- Murphy, K. Machine Learning: A Probabilistic Perspective. MIT Press
- 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 5) (Some ideas from instructors), and some ideas from a Stanford ML course, last year's course : 32%
- Project proposal report : 3%
- Phase-I presentation : 5%
- Phase-II presentation : 5%
- Final project 3 minute madness [See 3MT for inspiration] : 5%
- Final project demo and poster : 10%
- Final report : 4%
- 3 Quizzes worth 22% total:
- Quiz 1 : 6%
- Quiz 2: 8%
- Quiz 3: 8%
- End semester : 10%
- 6 Programming Homework Assignments (No credit for late submission) [with viva] : 36%
- One of these might include a Kaggle competition/Eval-AI