A complete, exam-ready study hub covering agents, search algorithms, heuristics, uncertainty, Bayesian reasoning, game theory, constraint satisfaction, and AI ethics - explained from first principles to advanced application, with diagrams, code, and 300+ practice questions.
From uninformed search to ethical deployment - each module builds directly on the last, exactly as examined in 24CSEN2151.
Definition & scope of AI, history, Narrow vs General Intelligence, agents & environments, rationality, problem-solving agents, and uninformed search: BFS, DFS, UCS.
Greedy Best-First & A* search, heuristic design, admissibility & consistency, hill climbing, simulated annealing, genetic algorithms, PSO, non-deterministic & partially observable search, online agents.
Sources of uncertainty, Bayesian networks (representation, learning, inference), decision theory & utility theory, Markov Decision Processes, value & policy iteration.
Game theory, Nash equilibrium, Minimax, Alpha-Beta pruning, Monte Carlo Tree Search, stochastic & partially observable games, backtracking & local search for CSPs.
AI in finance & marketing, healthcare diagnosis & analytics, predictive policing & law enforcement, and the ethical considerations that govern responsible AI deployment.
Short answer, long answer, and interview-style questions organized by module - the exact question patterns university and competitive exams favor.
Jump straight to any algorithm, theorem, or definition from the search bar above.
Every topic you open is remembered locally - watch your module completion bar fill up.
200+ auto-graded multiple choice questions with instant explanations, filterable by module.
Every search tree, flowchart, and state-space diagram is rendered live in HTML/CSS - not screenshots.
11 hands-on programming experiments accompany the theory (3-0-2-0-0-4 scheme: 2 practical hours/week).
| S.No | Experiment | Type |
|---|---|---|
| 1 | Revisit / refresh Python and PROLOG fundamentals (or any equivalent language) | Lab |
| 2 | Program to control Vacuum Cleaner agent moves (intelligent system design process) | Lab |
| 3 | Program to solve the Monkey & Banana problem | Lab |
| 4 | Program to solve the Water-Jug problem (PROLOG) | Lab |
| 5 | Program to solve the 8-puzzle problem using heuristics | Lab |
| 6 | Shortest path problem - (i) using BFS (ii) using Lowest-Cost-First Search | Lab |
| 7 | Implement Tic-Tac-Toe (Minimax algorithm & Alpha-Beta pruning) | Lab |
| 8 | Implement a Hangman game (or Wordle) | Lab |
| 9 | Propositional logic using kanren, SymPy, and pyDatalog packages in Python | Lab |
| 10 | Inferential logic using kanren, SymPy, and pyDatalog packages in Python | Lab |
| 11 | Binary classification using Decision Trees | Lab |
| # | Textbook |
|---|---|
| T1 | Stuart J. Russell & Peter Norvig - Artificial Intelligence: A Modern Approach, 4th Ed., Pearson, 2022 (ISBN 978-9356063570) |
| T2 | David L. Poole & Alan K. Mackworth - Artificial Intelligence: Foundations of Computational Agents, 2nd Ed., Cambridge University Press, 2017 (ISBN 978-1009258197) |
| R1 | George F. Luger - Artificial Intelligence, 6th Ed., 2017 |
| R2 | Elaine Rich, Kevin Knight, Shivashankar B. Nair - Artificial Intelligence, 2008 |
| R3 | Dan W. Patterson - Introduction to Artificial Intelligence and Expert Systems |
| R4 | Poole & Mackworth, free online text - artint.info/3e/html/ArtInt3e.html |
| R5 | UC Berkeley CS188 - inst.eecs.berkeley.edu/~cs188/sp19 |
| R6 | Microsoft AI Lab Projects - microsoft.com/en-us/ai/ai-lab-projects |
| R7 | NPTEL - Artificial Intelligence - nptel.ac.in/courses/106/105/106105079 |