ES 335: Machine Learning

August 2025 • IIT Gandhinagar

Welcome to ES 335: Machine Learning

Join our Slack workspace for course announcements and discussions

Course Information

Instructor: Prof. Nipun Batra
Email: nipun.batra@iitgn.ac.in
Office: 13/401C
Directions to office

Venue: Room 10/103

Teaching Assistants:

• Palak Gupta (palak.gupta@iitgn.ac.in)
• Abhyudaya Nair (abhyudaya.nair@iitgn.ac.in)
  
• Dekka Muni Kumar (muni.kumar@iitgn.ac.in)
• Diya Ashvinsinh Thakor (diya.thakor@iitgn.ac.in)
• Vinayak Rana (vinayak.rana@iitgn.ac.in)
• Rishabh Mondal (rishabh.mondal@iitgn.ac.in)
• Ayush Shrivastava (shrivastavaayush@iitgn.ac.in)
• Kaloori Shiva Prasad (kaloori.shiva@iitgn.ac.in)
• Suruchi Hardaha (suruchi.hardaha@iitgn.ac.in)
• Prathamesh Shanbhag (prathamesh.shanbhag@iitgn.ac.in)
• Aditya Mehta (aditya.mehta@iitgn.ac.in)
• Guntas Singh Saran (guntassingh.saran@iitgn.ac.in)
• Neerja Kasture (neerja.kasture@iitgn.ac.in)
• Akash Gupta (akash.gupta@iitgn.ac.in)
• Umang Shikarvar (umang.shikarvar@iitgn.ac.in)
• Venkatakrishnan E (venkatakrishnan.e@iitgn.ac.in)

Prerequisite Exam

The prerequisite exam must be submitted by August 10th, 2025 at 10:00 PM.

• The exam is open book and open internet
• Submission instructions are provided within the exam
• Mandatory for course enrollment

Prerequisites

Required Knowledge:

• Python Programming - Good experience with data structures and algorithms
• Probability & Statistics - Basic concepts and distributions
• Linear Algebra - Vectors, matrices, and operations
• Calculus - Derivatives and basic optimization

Preparation Materials

To refresh your understanding before the course begins:

• Python Data Science Handbook (Chapters 1-4)
• Linear Algebra for Machine Learning
• Khan Academy: Statistics and Probability
• Mathematics for Machine Learning (Free PDF)

Reference Textbooks

Primary References:

1. Mathematics for Machine Learning - Deisenroth, Faisal, Ong • Free PDF

2. An Introduction to Statistical Learning - James, Witten, Hastie, Tibshirani • Free Online

3. Pattern Recognition and Machine Learning - Christopher Bishop • Free PDF

Additional References:

4. The Elements of Statistical Learning - Friedman, Hastie, Tibshirani • Free PDF

5. Deep Learning - Goodfellow, Bengio, Courville • Free Online

6. Pattern Classification - Duda, Hart, Stork

7. Machine Learning - Tom Mitchell

8. Machine Learning: A Probabilistic Perspective - Kevin Murphy

Additional Learning Resources

Online Courses:

• NPTEL Machine Learning - Balaram Ravindran
• CMU Machine Learning - Mitchell & Balcan
• Coursera ML Course - Andrew Ng
• Fast.ai Machine Learning
• Fast.ai Deep Learning
• UCI Machine Learning - Alex Ihler