Principles of AI

BTech First Year Course

NoteCourse Information

Instructor: Prof. Nipun Batra, IIT Gandhinagar

Semester: January 2026

Target: First-year BTech students (no ML background assumed)

Repository: github.com/nipunbatra/principles-ai-teaching

About This Course

A practical introduction to AI and Machine Learning designed for beginners. We build intuition first, then formalize with math and code. By the end, you’ll understand how ChatGPT works and be able to build simple AI systems yourself.

Lectures

# Topic Slides Key Concepts
1 What is AI? — AI landscape, applications, what we’ll build
2 Data Foundation HTML ∙ PDF ML paradigms, features/labels, train/test split, sklearn
3 Supervised Learning HTML ∙ PDF Linear/Logistic regression, trees, KNN, metrics
4 Model Selection HTML ∙ PDF Overfitting, cross-validation, bias-variance
5 Neural Networks HTML ∙ PDF Perceptrons, backprop, PyTorch basics
6 Computer Vision HTML ∙ PDF CNNs, object detection, YOLO
7 Language Models HTML ∙ PDF Next-token prediction, embeddings, attention, transformers

Labs / Demos

Interactive Jupyter notebooks for hands-on practice.

Lecture Notebook Description
L02 L02_data_foundation.ipynb sklearn API, train/test, clustering
L03 L03_supervised_learning.ipynb Regression, classification, metrics
L04 L04_model_selection.ipynb Cross-validation, grid search
L05 L05_neural_networks.ipynb PyTorch, training loops, MNIST
L06 L06_computer_vision.ipynb CNNs, YOLO inference
L07 L07_language_models.ipynb Tokenization, bigrams, embeddings

Course Project

Project Notebook Goal
Build Your Own LM PROJECT_simple_slm.ipynb Train a character-level language model on Shakespeare

Additional Resources

TipLinks

Course Materials by Prof. Nipun Batra | IIT Gandhinagar

Last updated: January 2026