# ES 654 Machine Learning

### Summary

- Instructor: Nipun Batra (nipun.batra@iitgn.ac.in)
- 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

### 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]