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On this page

  • Overview
  • IIT Gandhinagar Courses
    • Current & Recent Offerings
    • Historical Course Offerings
  • Course Evolution & Features
    • Video Resources
    • Course Structure Evolution
    • Global Usage
  • For Instructors
    • Using These Materials
    • Customization Guidelines
  • Student Testimonials
  • About the Instructor
  • Contributing to Course Development

Other Formats

  • Jupyter

Courses Using These Materials

Machine learning courses at IIT Gandhinagar and beyond

Overview

These comprehensive machine learning materials have been developed and refined through multiple course offerings at IIT Gandhinagar and are used by instructors worldwide. Below are the primary courses that utilize and contribute to this resource collection.


IIT Gandhinagar Courses

Current & Recent Offerings

Fall 2025 - Machine Learning
Latest iteration of the comprehensive ML course with cutting-edge content and methodologies.

Course Website GitHub Repository

Spring 2024 - Machine Learning (ES335)
Comprehensive ML course with YouTube video lectures covering all major topics from basics to advanced techniques.

Course Website YouTube Lectures Assignments

Fall 2023 - Probabilistic Machine Learning
Advanced course focusing on probabilistic approaches to machine learning with video lectures.

Course Website YouTube Lectures

Historical Course Offerings

Spring 2023 - Machine Learning
Machine learning fundamentals with practical implementations and real-world applications.

Course Website

Spring 2022 - Machine Learning
Comprehensive coverage of supervised and unsupervised learning techniques.

Course Website

Spring 2021 - Machine Learning
ML course adapted for online learning during the pandemic with enhanced digital resources.

Course Website

Spring 2020 - Machine Learning
Foundation course with comprehensive video lectures covering core ML concepts and algorithms.

Course Website YouTube Lectures

Spring 2019 - Machine Learning
Early iteration of the course that established many of the pedagogical approaches used today.

Course Website


Course Evolution & Features

Video Resources

Several course iterations include comprehensive YouTube video lectures:

  • Spring 2024: Complete lecture series with assignments and interactive content
  • Fall 2023: Probabilistic ML focus with advanced mathematical treatments
  • Spring 2020: Foundational lectures that established the video-based learning approach
Accessing Video Content

Video lectures are integrated into the respective course websites. Visit the course links above and look for video icons or lecture sections to access the YouTube playlists.

Course Structure Evolution

Each course iteration has contributed to the materials in this repository:

  • Slides: LaTeX-based presentations refined across multiple offerings
  • Notebooks: Interactive Jupyter notebooks with hands-on examples
  • Assignments: Progressive problem sets using GitHub Classroom
  • Tutorials: Step-by-step guides for complex topics

Global Usage

These materials are designed for:

  • University Courses: Adoptable by instructors worldwide
  • Self-Study: Complete learning path for independent learners
  • Corporate Training: Professional development programs
  • Research Groups: Foundation for ML research education

For Instructors

Using These Materials

Course Templates

Complete semester-long courses with syllabi, schedules, and assessment strategies.

Browse Courses

Video Integration

Learn how to incorporate video lectures and online components into your ML course.

Video Resources

Assessment Tools

Assignments, projects, and evaluation rubrics tested across multiple course offerings.

View Assignments

Customization Guidelines

  • Modular Design: Pick and choose topics based on your course duration
  • Difficulty Scaling: Materials range from introductory to advanced graduate level
  • Platform Agnostic: Works with any LMS or course website platform
  • Regular Updates: Materials continuously improved based on student feedback
Instructor Support

If you’re using these materials in your course, we’d love to hear about it! Share your experience and join our community of ML educators.


Student Testimonials

“The combination of theoretical slides, hands-on notebooks, and video lectures made complex ML concepts accessible and engaging.” - Spring 2024 Student

“The progression from basic concepts to advanced topics was perfectly paced. The assignments really helped solidify the learning.” - Fall 2023 Student

“Having access to multiple years of materials allowed me to find different explanations for challenging topics.” - Self-study Learner


About the Instructor

Prof. Nipun Batra is an Associate Professor in Computer Science at IIT Gandhinagar. His research group works on machine learning/AI/sensors/IoT for computational sustainability problems.

  • Faculty Profile
  • Research Group
  • All Courses

Contributing to Course Development

Found ways to improve the course materials? We welcome contributions from the global ML education community:

  • Course Feedback: Share experiences using these materials in your courses
  • Content Improvements: Suggest updates or additional topics
  • New Formats: Help adapt materials for different learning contexts
  • Translation: Assist with creating materials in other languages

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These courses represent years of pedagogical refinement and are continuously updated to reflect best practices in machine learning education.

ML Resources: From Code to Model
Open Educational Materials

Copyright 2025

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