Machine Learning Spring 2022
ES 654
- Instructor: Nipun Batra (nipun.batra@iitgn.ac.in)
- Teaching Assistants: Zeel Patel, Rishiraj Adhikary (rishiraj.a@iitgn.ac.in), Shriraj Sawant, Hetvi Shastri
- Course Timings: Monday (10:05 AM - 11 AM), Wednesday (9:05 AM - 10 AM), Thursday (10:05 - 11 AM)
- Google Classroom code: jclv62l
- Zoom link: https://iitgn-ac-in.zoom.us/j/96523429150
Course FAQ, grading policy: You have comment access, please comment for more details.
List of project ideas: You have comment access, please comment to ask the TAs for more details.
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]
Some other ML courses