Artificial Intelligence detailed syllabus scheme for Information Technology (IT), 2020-21 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 7th Sem Scheme of Information Technology (IT), 2020-21 Onwards, do visit IT 7th Sem Scheme, 2020-21 Onwards. For the Elective-VII Labs scheme of 7th Sem 2020-21 onwards, refer to IT 7th Sem Elective-VII Labs Scheme 2020-21 Onwards. The detail syllabus for artificial intelligence is as follows.
Artificial Intelligence Syllabus for Information Technology (IT) 4th Year 7th Sem 2020-21 DBATU
Course Outcomes:
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Course Objectives:
- To acquaint the students with the theoretical and computational techniques in Artificial Intelligence.
- To use various symbolic knowledge representation to specify domains and reasoning tasks of a situated software agent.
- To use different logical systems for inference over formal domain representations, and trace how a particular inference algorithm works on a given problem specification.
- To understand the conceptual and computational trade-offs between the expressiveness of different formal representations.
Unit I
Introduction: Overview of Artificial intelligence- Problems of AI, AI technique, Tic – Tac – Toe problem. Intelligent Agents: Agents and environment, nature of environment, structure of agents, goal based agents, utility based agents, learning agents.
Unit II
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Unit III
Heuristic search strategies: Greedy best-first search, A* search, memory bounded heuristic search: local search algorithms and optimization problems: Hill climbing search, simulated annealing search, local beam search, genetic algorithms; constraint satisfaction problems, local search for constraint satisfaction problems.
Adversarial search: Games, optimal decisions and strategies in games, the minimax search procedure, alpha-beta pruning, additional refinements, iterative deepening.
Unit IV
Knowledge and reasoning: Knowledge representation issues, representation and mapping, approaches to knowledge representation, issues in knowledge representation.
Representing knowledge using rules: Procedural verses declarative knowledge, logic programming, forward verses backward reasoning, matching, control knowledge.
Unit V
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Unit VI
Natural Language processing: Introduction, Syntactic processing, semantic analysis, discourse and pragmatic processing. Learning: Forms of learning, inductive learning, learning decision trees, explanation based learning, learning using relevance information, neural net learning and genetic learning.
Expert Systems: Representing and using domain knowledge, expert system shells, and knowledge acquisition.
Text Books:
- Rich, E. and Knight, K., ArtificialIntelligence , Tata McGraw- Hill.
- Russell, S. and Norvig, P., ArtificialIntelligence: A Modern Approach , Pearson Education.
- Patterson, Dan W. , Introduction to Artificial Intelligence and Expert Systems, Patterson, PHI, 2005
Reference Book:
- Nilsson, N. J., Artificial Intelligence: A New Synthesis , Morgan Kaufmann.
For detail syllabus of all subjects of Information Technology (IT) 7th Sem 2020-21 onwards, visit IT 7th Sem Subjects of 2020-21 Onwards.