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Artificial Intelligence Semester 7 (BE Fourth Year) BE Computer Engineering University of Mumbai Topics and Syllabus

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University of Mumbai Syllabus For Semester 7 (BE Fourth Year) Artificial Intelligence: Knowing the Syllabus is very important for the students of Semester 7 (BE Fourth Year). Shaalaa has also provided a list of topics that every student needs to understand.

The University of Mumbai Semester 7 (BE Fourth Year) Artificial Intelligence syllabus for the academic year 2021-2022 is based on the Board's guidelines. Students should read the Semester 7 (BE Fourth Year) Artificial Intelligence Syllabus to learn about the subject's subjects and subtopics.

Students will discover the unit names, chapters under each unit, and subtopics under each chapter in the University of Mumbai Semester 7 (BE Fourth Year) Artificial Intelligence Syllabus pdf 2021-2022. They will also receive a complete practical syllabus for Semester 7 (BE Fourth Year) Artificial Intelligence in addition to this.

CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

University of Mumbai Semester 7 (BE Fourth Year) Artificial Intelligence Revised Syllabus

University of Mumbai Semester 7 (BE Fourth Year) Artificial Intelligence and their Unit wise marks distribution

University of Mumbai Semester 7 (BE Fourth Year) Artificial Intelligence Course Structure 2021-2022 With Marking Scheme

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Syllabus

100 Introduction to Artificial Intelligence
  • 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.
200 Intelligent Agents
  • Agents and Environments
  • The concept of rationality
  • The nature of environment
  • The structure of Agents
  • Types of Agents
  • Learning Agent
300 Problem Solving
  • Solving problem by Searching:- Problem Solving Agent, Formulating Problems, Example Problems.
  • 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.
  • Local Search Algorithms and Optimization Problems:- Hillclimbing search Simulated annealing, Local beam search, Genetic algorithms.
  • Adversarial Search:- Games, Optimal strategies, The minimax algorithm, Alpha-Beta Pruning.
400 Knowledge and Reasoning
  • Knowledge based Agents, The Wumpus World, The Propositional logic, First Order Logic:- Syntax and Semantic, Inference in FOL, Forward chaining, backward Chaining.
  • Knowledge Engineering in First-Order Logic, Unification, Resolution, Introduction to logic programming (PROLOG).
  • Uncertain Knowledge and Reasoning:- Uncertainty, Representing knowledge in an uncertain domain, The semantics of belief network, Inference in belief network.
500 Planning and Learning
  • The planning problem, Planning with state space search, Partial order planning, Hierarchical planning, Conditional Planning.
  • Learning:- Forms of Learning, Inductive Learning, Learning Decision Tree.
  • Expert System:- Introduction, Phases in building Expert Systems, ES Architecture, ES vs Traditional System.
600 Applications
  • Natural Language Processing(NLP), Expert Systems.
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