B.E. (Computer Engineering) Artificial Intelligence Syllabus - Mumbai University 2026
The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.
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Syllabus for Artificial Intelligence
Module 1: Introduction to Artificial Intelligence
- 1.1 Introduction, History of Artificial Intelligence, Intelligent Systems: Categorization of Intelligent System, Components of AI Program, Foundations of AI, Sub-areas of AI, Applications of AI, Current trends in AI.
Module 2: Intelligent Agents
- 2.1 Agents and Environments, The concept of rationality, The nature of environment, The structure of Agents, Types of Agents, Learning Agent.
- 2.2 Solving problem by Searching: Problem Solving Agent, Formulating Problems, Example Problems.
Module 3: Problem solving
- 3.1 Uninformed Search Methods: Breadth First Search (BFS), Depth First Search (DFS), Depth Limited Search, Depth First Iterative Deepening (DFID), Informed Search Methods: Greedy best first Search, A* Search, Memory bounded heuristic Search.
- 3.2 Local Search Algorithms and Optimization Problems: Hill climbing search Simulated annealing, Genetic algorithms. Adversarial Search: Game Playing, Min-Max Search, Alpha Beta Pruning
Module 4: Knowledge and Reasoning
- 4.1 Knowledge based Agents, Brief Overview of propositional logic, First Order Logic: Syntax and Semantic, Inference in FOL, Forward chaining, backward Chaining.
- 4.2 Knowledge Engineering in First-Order Logic, Unification, Resolution
- 4.3 Uncertain Knowledge and Reasoning: Uncertainty, Representing knowledge in an uncertain domain, The semantics of belief network, Simple Inference in belief network
Module 5: Planning and Learning
- 5.1 The planning problem, Planning with state space search, Partial order planning, Hierarchical planning, Conditional Planning.
- 5.2 Learning: Forms of Learning, Theory of Learning, PAC learning. Introduction to statistical learning (Introduction only) Introduction to reinforcement learning: Learning from Rewards, Passive Reinforcement Learning, Active reinforcement Learning
Module 6: AI Applications
- A. Introduction to NLP- Language models, Grammars, Parsing
- B Robotics - Robots, Robot hardware, Problems Robotics can solve
- C AI applications in Healthcare, Retail, Banking
Useful Links
- 1 Stuart J. Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach”, Fourth Edition" Pearson Education, 2020.
- 2 Saroj Kaushik, “Artificial Intelligence”, Cengage Learning, First edition, 2011
- 3 George F Luger, “ Artificial Intelligence” Low Price Edition, Fourth edition, Pearson Education.,2005
- 1 Nils J. Nilsson, Principles of Artificial Intelligence, Narosa Publication.
- 2 Deepak Khemani, A First Course in Artificial Intelligence, McGraw Hill Publication
- 3 Patrick H. Winston, Artificial Intelligence, 3rd edition, Pearson Education.
- 4 Elaine Rich and Kevin Knight, "Artificial Intelligence”, Third Edition, McGraw Hill Education,2017.
- 1 https://nptel.ac.in/courses/106/105/106105078/
- 2 https://thestempedia.com/blog/simple-ai-and-machine-learning-projects-for-students and-beginners/
- 3 https://nptel.ac.in/courses/106/105/106105079/
Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2021-22. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.