Machine Learning
ES 335 · IIT Gandhinagar · Semester I, 2024–25
The August 2024 offering of ES 335. Its 33 lectures run from decision trees and linear models through optimisation, classification and neural networks to unsupervised learning and support vector machines. Every lecture links its slides and notebooks.
- Instructor
- Prof. Nipun Batra
- Office
- 13/401C (directions)
- Venue
- Room 10/103, for lectures and tutorials
- nipun.batra@iitgn.ac.in
- Teaching assistants
- 11 TAs
- Prerequisite test
- Open-book test, required to take the course
- Discussion
- Course Slack
This offering ran from August to November 2024. Its lectures, slides and notebooks remain available.
- Lectures
- 33
- Slide decks
- 28
- Notebooks
- 37
- Quizzes
- 4, best 3 count
Lectures
- 1–2Foundations2 lectures
- 3–7Trees, model fit and ensembles5 lectures
- 8–14Linear models and optimisation7 lectures
- 15–18Classification4 lectures
- 19–26Neural networks8 lectures
- 27–29Unsupervised learning, neighbours and reinforcement learning3 lectures
- 30–33Support vector machines4 lectures
Questions
Is there a final project?
No separate one. Each assignment has a project-like question instead, the same for everyone, so the added structure helps you get started and you learn the ideas by applying them.
What are the prerequisites?
Python programming with data structures (ES 242 or equivalent), probability and statistics, linear algebra and calculus. The prerequisite test checks these; you must clear it to take the course.
What if I miss a quiz?
A missed quiz scores zero and there are no makeups. The best three of four quizzes count, which is designed for such cases.
How are assignments graded?
Assignments are done in groups and followed by a viva. Marks are split between code functionality, your understanding, code quality and theory. See Assignment marks.
Can I use my laptop in class?
No. Laptops are not allowed in class for any reason.
All questions · or ask on the course Slack.
References
Reference textbooks:
- Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Mathematics for Machine Learning (free PDF)
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R (online edition)
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006 (free PDF)
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning, Springer, 2001 (free PDF)
- Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, Wiley
- Tom M. Mitchell, Machine Learning, McGraw Hill, 1997
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016 (online edition)
Preparation material, to refresh the prerequisites before the course begins:
- Python Data Science Handbook, chapters 1 to 4
- Linear Algebra for Machine Learning
- Khan Academy: Statistics and Probability
- Mathematics for Machine Learning (free PDF)
Other courses:
- NPTEL Machine Learning, Balaraman Ravindran
- CMU Machine Learning, Tom Mitchell and Maria-Florina Balcan
- Coursera Machine Learning, Andrew Ng
- fast.ai Machine Learning and Practical Deep Learning for Coders
- UCI Machine Learning, Alex Ihler
Teaching assistants
- Anupam Sharmasharmaanupam@iitgn.ac.in
- Ayush Shrivastavashrivastavaayush@iitgn.ac.in
- Eshwar Dhandeeshwar.dhande@iitgn.ac.in
- Isha Narangisha.narang@iitgn.ac.in
- Prince Kumarprince.kumar@iitgn.ac.in
- Sawan Vermasawan.verma@iitgn.ac.in
- Shataxi Dubeyshataxi.dubey@iitgn.ac.in
- Aadya Aroraaadya.arora@iitgn.ac.in
- Bhoumik Patidarbhoumik.patidar@iitgn.ac.in
- Guntas Singh Saranguntassingh.saran@iitgn.ac.in
- Hrriday Ruparelhrriday.ruparel@iitgn.ac.in