Artificial Intelligence ( AI ) Syllabus | MCS | Lincoln University College

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Introduction:

  • Intelligence, Artificial Intelligence (AI), AI Perspectives: acting and thinking humanly, acting and thinking rationally
  • History of AI
  • Foundations of AI: Philosophy, Economics, Psycology, Sociology, Linguistics
  • Neuroscience, Mathmatics, Computer Science, Control Theory
  • Applications of AI

Intelligent Agents:

  • Introduction of agents, Structure of Intelligent agent, Properties of Intelligent Agents
  • Configuration of Agents, PEAS description of Agents, PAGE
  • Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Learning Agent
  • Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent

Problem Solving by Searching:

  • Definition, State space representation, Problem as a state space search, Problem formulation, Welldefined problems
  • Solving Problems by Searching, Search Strategies: Informed, Uninformed, Performance evaluation of search strategies: Time Complexity, Space Complexity, Completeness, Optimality
  • Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Uniform Cost Search,
  • Bidirectional Search
  • Informed Search, Heuristic Function, Admissible Heuristic, Informed Search Techniques: Greedy Best First Search, A* Search, Optimality and Admissibility in A*, Hill Climbing Search, Simulated Annealing Search
  • Game Playing, Adversarial Search Techniques: Mini-max Search, Alpha-Beta Pruning
  • Constraint Satisfaction Problems, Examples of Constraint Satisfaction Problems

Knowledge Representation:

  • Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems,
  • Properties ofKnowledge Representation Systems
  • Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual
  • Dependencies, Scripts, Rule Based Systems (Production System), Propositional Logic, Predicate Logic
  • Propositional Logic(PL): Syntax, Semantics, Formal logic-connectives, truth tables, tautology, validity, well-formed- formula, Inference using Resolution, Backward Chaining and Forward Chaining
  • Predicate Logic: FOPL, Syntax, Semantics, Quantification, Inference with FOPL: By converting into PL (existential and universal instantiation), Unification and lifting, Inference using resolution

Machine Learning:

  • Introduction to Machine Learning, Concepts of Learning, Supervised, Unsupervised and
  • Reinforcement Learning
  • Statistical-based Learning: Naive Bayes Model
  • Learning by Genetic Algorithms: Operators in
  • Genetic Algorithm: Selection, Mutation,
  • Crossover, Fitness Function, Genetic Algorithm
  • Learning with Neural Networks: Introduction, Biological Neural Networks Vs. Artificial Neural Networks (ANN), Mathematical Model of ANN, Activation Functions: Linear, Step Sigmoid, Types of ANN: Feed-forward, Recurrent, Single Layered,
  • Multi-Layered, Application of Artificial Neural Networks, Learning by Training ANN, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back- propagation Learning

Applications of AI:

  • Expert Systems, Components of Expert System: Knowledge base, inference engine, user interface, working memory, Development of Expert Systems
  • Natural Language Processing: Natural Language
  • Understanding and Natural Language Generation, Steps of Natural Language Processing: Lexical Analysis(Segmentation, Morphological Analysis), Syntatic Analysis, Semantic Analysis, Pragmatic Analysis, Machine Translation,
  • Machine Vision Concepts: Machine vision and its applications, Components of Machine Vision System
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