- Chapter 1 : Introduction
- The Foundations of Artificial Intelligence
- The History and Evolution of Artificial Intelligence
- The State-of-the-Art: Models
- Agents of AI
- Hype vs Reality
- Day-to-day interactions with AI
- Types of AI : Rule-based & Learning-based systems
- Neural Networks – The Brain Behind Artificial Intelligence
- Reinforcement Learning
- Generative AI
- GAN (Generative Adversarial Network)- Training and Fine-tuning
- AI tools
- Agentic AI
- Chapter 2 : Solving Problem by Searching
- Problem Solving Agents
- Informed and Uninformed (Blind) Search
- Basics to understand searches
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Uniform-Cost Search (UCS) – Romania Map Problem
- 8-Puzzle Problem
- 8 Puzzle problem with Heuristic 1 – Misplaced tiles
- 8 Puzzle problem with Heuristic 2 – Manhattan distance
- Additional Practice Problems: 8-Puzzle
- Solving a Maze using BFS and DFS
- Best-First Search (BFS)
- Understanding Heuristic Values in Best-First Search
- A* Algorithm
- Overestimation and Underestimation in Heuristic in A*
- Chapter 3 : Machine Learning
- Chapter 4 : AI Ethics & Responsible AI
- Chapter 5 :Introduction to Natural Language Processing (NLP)
- Introduction
- Basic Terminologies in NLP
- Text Processing in NLP
- Word Embeddings in Natural Language Processing (NLP)
- one Hot encoding
- Bog of words
- TF-IDF (Term Frequency – Inverse Document Frequency)
- Word2Vec in Natural Language Processing
- Drawbacks of Word2Vec
- BERT Transformer Model
- Prompt Engineering
- Case study-Flood Prediction
- Robotics and Automation