ES 335 Machine Learning (August 2024)
Please join the Slack channel for the course.
Summary
- Instructor: Nipun Batra nipun.batra@iitgn.ac.in
- Office: 13/401C
- YouTube video showing the directions to my office
- Teaching Assistants:
- Ayush Shrivastava shrivastavaayush@iitgn.ac.in
- Course Timings:
- Lectures: 10/103
- Tutorials: 10/103
Pre-requisite exam
The pre-requisite exam has to be submitted by 6th August 2024, 9 PM. Details on how to submit the exam are given in the exam itself. The exam is open book and open internet.
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
- Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. [Freely available online]
Reference textbooks:
- Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. [Freely available online]
- 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]