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Reason:Post: ARTIFICIAL INTELLIGENCE Ebooks, presentations and lecture notes covering full semester syllabusIts fake....remove it...
Forum: Computer Science Engineering Ebooks Download/ Computer Science Engineering Notes
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Posted by: FaaDoOEngineer
Original Content:The topics covered in the attached e-books are:
UNIT - I
Introduction : AI problems, foundation of AI and history of AI intelligent agents: Agents and Environments,the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation.
UNIT - II
Searching : Searching for solutions, uniformed search strategies – Breadth first search, depth first Search. Search with partial information (Heuristic search) Greedy best first search, A* search Game Playing: Adversial search, Games, minimax, algorithm, optimal decisions in multiplayer games, Alpha-Beta pruning, Evaluation functions, cutting of search.
UNIT - III
Knowledge Representation & Reasons logical Agents, Knowledge – Based Agents, the Wumpus world, logic, propositional logic, Resolution patterns in propos ional logic, Resolution, Forward & Backward. Chaining.
UNIT - IV
First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution.
UNIT - V
Characteristics of Neural Networks, Historical Development of Neural Networks Principles, Artificial Neural Networks: Terminology, Models of Neuron, Topology, Basic Learning Laws, Pattern Recognition Problem, Basic Functional Units, Pattern Recognition Tasks by the Functional Units.
UNIT - VI
Feedforward Neural Networks:
Introduction, Analysis of pattern Association Networks, Analysis of Pattern Classification Networks, Analysis of pattern storage Networks. Analysis of Pattern Mapping Networks.
UNIT - VII
Feedback Neural Networks
Introduction, Analysis of Linear Autoassociative FF Networks, Analysis of Pattern Storage Networks.
UNIT - VIII
Competitive Learning Neural Networks & Complex pattern Recognition
Introduction, Analysis of Pattern Clustering Networks, Analysis of Feature
Mapping Networks, Associative Memory.