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Home / Syllabus / B.E. (Artificial Intelligence and Data Science) / TY BE AI and DS / Semester 5 / Artificial Intelligence

B.E. (Artificial Intelligence and Data Science) Artificial Intelligence Syllabus - Mumbai University

This is the TY BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01815 and 1T01816. The first and second years of the degree are on NEP 2020.

Artificial Intelligence.pdf
Semester 5 · TY BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Artificial Intelligence

Semester 5 · TY BE AI and DS · 3 credits · CBCS REV-2019 'C' Scheme

Module 1 3 hours

  • Introduction to Artificial Intelligence
  • 1.1 Artificial Intelligence (AI), AI Perspectives: Acting and Thinking humanly, Acting and Thinking rationally
  • 1.2 History of AI, Applications of AI, The present state of AI, Ethics in AI

Module 2 4 hours

  • Intelligent Agents
  • 2.1 Introduction of agents, Structure of Intelligent Agent, Characteristics of Intelligent Agents
  • 2.2 Types of Agents: Simple Reflex, Model Based, Goal Based, Utility Based Agents.
  • 2.2 Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent

Module 3 12 hours

  • Solving Problems by Searching
  • 3.1 Definition, State space representation, Problem as a state space search, Problem formulation, Well-defined problems
  • 3.2 Solving Problems by Searching, Performance evaluation of search strategies, Time Complexity, Space Complexity, Completeness, Optimality
  • 3.3 Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search, Bidirectional Search
  • 3.4 Informed Search: Heuristic Function, Admissible Heuristic, Informed Search Technique, Greedy Best First Search, A* Search, Local Search: Hill Climbing Search, Simulated Annealing Search, Optimization: Genetic Algorithm
  • 3.5 Game Playing, Adversarial Search Techniques, Mini-max Search, Alpha-Beta Pruning

Module 4 10 hours

  • Knowledge and Reasoning
  • 4.1 Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems
  • 4.2 Propositional Logic (PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Introduction to logic programming (PROLOG)
  • 4.3 Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference rules in FOPL,
  • 4.4 Forward Chaining, Backward Chaining and Resolution in FOPL

Module 5 5 hours

  • Reasoning Under Uncertainty
  • Handling Uncertain Knowledge, Random Variables, Prior and Posterior Probability, Inference using Full Joint Distribution
  • Bayes' Rule and its use, Bayesian Belief Networks, Reasoning in Belief Networks

Module 6 5 hours

  • Planning and Learning
  • 6.1 The planning problem, Partial order planning, total order planning.
  • 6.2 Learning in AI, Learning Agent, Concepts of Supervised, Unsupervised, Semi -Supervised Learning, Reinforcement Learning, Ensemble Learning.
  • 6.3 Expert Systems, Components of Expert System: Knowledge base, Inference engine, user interface, working memory, Development of Expert Systems

Text Books

  • 1 Stuart J. Russell and Peter Norvig, "Artificial Intelligence A Modern Approach "Second Edition" Pearson Education.
  • 2 Elaine Rich and Kevin Knight "Artificial Intelligence" Third Edition, Tata McGraw-Hill Education Pvt. Ltd., 2008.
  • 3 George F Luger "Artificial Intelligence" Low Price Edition, Pearson Education., Fourth edition.

References

  • 1 Ivan Bratko "PROLOG Programming for Artificial Intelligence", Pearson Education, Third Edition.
  • 2 D. W. Patterson, Artificial Intelligence and Expert Systems, Prentice Hall.
  • 3 Saroj Kaushik "Artificial Intelligence", Cengage Learning.
  • 4 Davis E. Goldberg, "Genetic Algorithms: Search, Optimization and Machine Learning", Addison Wesley, N.Y., 1989.
  • 5 Patrick Henry Winston, "Artificial Intelligence", Addison-Wesley, Third Edition.
  • 6 N. P. Padhy, "Artificial Intelligence and Intelligent Systems", Oxford University Press.

Useful Links

  • 1 An Introduction to Artificial Intelligence - Course (nptel.ac.in)
  • 2 NPTEL
  • 3 https://www.classcentral.com/course/independent-elements-of-ai-12469
  • 4 https://tinyurl.com/ai-for-everyone

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.42 (R), under CBCS REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording, module numbering and hours are as printed in that syllabus.

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.

PDF B.E. Artificial Intelligence and Data Science - First Year, Semester I and II - NEP 2020 - Item 7.7 (R-A) NEP 2020, Semesters I and II, in force from 2024-25 Read full PDF Read
PDF B.E. Artificial Intelligence and Data Science - Second Year, Semester III and IV - NEP 2020 - Item 6.20 (N) NEP 2020, Semesters III and IV, in force from 2025-26 Read full PDF Read
PDF B.E. Artificial Intelligence and Data Science - Third Year, Semester V and VI - CBCS REV-2019 C Scheme - Item 6.42 (R) CBCS REV-2019 'C' Scheme, Semesters V and VI, in force from 2022-23 Read full PDF Read
PDF B.E. Artificial Intelligence and Data Science - Fourth Year, Semester VII and VIII - CBCS REV-2019 C Scheme - Item 6.12 (N) CBCS REV-2019 'C' Scheme, Semesters VII and VIII, in force from 2023-24 Read full PDF Read
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