Machine Learning
ES 335 · IIT Gandhinagar · Semester I, 2025–26
ES 335 is an introduction to machine learning: how models learn from data, how to evaluate them honestly, and the main families of methods, from decision trees and linear models to neural networks, clustering and support vector machines. Every lecture links its slides, notebooks, cheat sheet and one-minute videos.
- Instructor
- Prof. Nipun Batra
- Office
- 13/401C (directions)
- Venue
- Room 10/103
- nipun.batra@iitgn.ac.in
- Teaching assistants
- 16 TAs
- Prerequisite test
- Open-book test, required to enrol
- Discussion
- Course Slack
This offering ran from August to November 2025. Its lectures, cheat sheets and quiz solutions remain available.
- Lectures
- 33
- Cheat sheets
- 28
- Quizzes
- 4, with solutions
- Assignments
- 3
Lectures
- 1–2Foundations2 lectures
- 3–7Trees, model fit and ensembles5 lectures
- 8–15Linear models and optimisation8 lectures
- 16–19Classification4 lectures
- 20–26Neural networks7 lectures
- 27–29Unsupervised and reinforcement learning3 lectures
- 30–33Neighbours and support vector machines4 lectures
- +Also in the series3 topics
Questions
Is there a final project?
No separate one. Each assignment has a project-like question instead, so everyone works on similar problems with better guidance, and learns the ideas by applying them.
What are the prerequisites?
Python with good experience of data structures and algorithms, basic probability and statistics, linear algebra and calculus. The prerequisite test checks these and is required to enrol.
How are assignments graded?
Assignments are done in groups, and each is followed by a viva. Marks are split between code functionality, your understanding, code quality and theory. See Grading.
What if I miss a quiz?
The best three of four quizzes count, and there are no makeup quizzes for any reason, so a missed quiz is the dropped one.
All questions · or ask on the course Slack.
References
There is no single textbook. Primary references:
- 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 (online edition)
- Christopher M. Bishop, Pattern Recognition and Machine Learning (free PDF)
Further reading:
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning (free PDF)
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (online edition)
- Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification
- Tom Mitchell, Machine Learning
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective
Preparation material:
- Python Data Science Handbook, chapters 1 to 4
- Linear Algebra for Machine Learning
- Khan Academy: Statistics and Probability
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 fast.ai Deep Learning
- UCI Machine Learning, Alex Ihler
Teaching assistants
- Palak Guptapalak.gupta@iitgn.ac.in
- Abhyudaya Nairabhyudaya.nair@iitgn.ac.in
- Dekka Muni Kumarmuni.kumar@iitgn.ac.in
- Diya Ashvinsinh Thakordiya.thakor@iitgn.ac.in
- Vinayak Ranavinayak.rana@iitgn.ac.in
- Rishabh Mondalrishabh.mondal@iitgn.ac.in
- Ayush Shrivastavashrivastavaayush@iitgn.ac.in
- Kaloori Shiva Prasadkaloori.shiva@iitgn.ac.in
- Suruchi Hardahasuruchi.hardaha@iitgn.ac.in
- Prathamesh Shanbhagprathamesh.shanbhag@iitgn.ac.in
- Aditya Mehtaaditya.mehta@iitgn.ac.in
- Guntas Singh Saranguntassingh.saran@iitgn.ac.in
- Neerja Kastureneerja.kasture@iitgn.ac.in
- Akash Guptaakash.gupta@iitgn.ac.in
- Umang Shikarvarumang.shikarvar@iitgn.ac.in
- Venkatakrishnan Evenkatakrishnan.e@iitgn.ac.in