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B.E Artificial Intelligence and Machine Learning Artificial Intelligence Syllabus - Mumbai University

This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Artificial-Intelligence.pdf
Semester 5 · Third Year AI-ML · 3 credits · 100 marks

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Syllabus for Artificial Intelligence

Semester 5 · Third Year AI-ML · 3 credits · 100 marks

Module 1: Introduction to Artificial Intelligence

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

Module 2: Intelligent Agents

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

Module 3: 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 Knowledge and Reasoning Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Properties of Knowledge Representation Systems Propositional Logic (PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed-formula, Introduction to logic programming (PROLOG) Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference rules in FOPL, Forward Chaining, Backward Chaining and Resolution in FOPL 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 Planning and Learning The planning problem, Partial order planning, total order planning. Learning in AI, Learning Agent, Concepts of Supervised, Unsupervised, Semi Supervised Learning, Reinforcement Learning, Ensemble Learning. Expert Systems, Components of Expert System: Knowledge base, Inference engine, user interface, working memory, Development of Expert Systems Total

Useful Links

  • 1 Stuart J. Russell and Peter Norvig, "Artificial Intelligence A Modern Approach ―Second Edition" Pearson Education.
  • 2 Elaine Rich and Kevin Knight ―Artificial Intelligenceǁ Third Edition, Tata McGraw-Hill Education Pvt. Ltd., 2008.
  • 3 George F Luger ―Artificial Intelligence‖ Low Price Edition, Pearson Education., Fourth edition.
  • 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.
  • 1 An Introduction to Artificial Intelligence - Course (nptel.ac.in)
  • 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 Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2022-23. 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.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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