Artificial Intelligence
Deep Understanding: 60 hours
The course introduces the ideas and techniques underlying the principles and design of artificial intelligent systems. It covers the basics and applications of AI, including design of intelligent agents, problem solving, searching, knowledge representation systems, probabilistic reasoning, neural networks, machine learning and natural language processing.
Introduce fundamental concepts of Artificial Intelligence, learn about computer systems that exhibit intelligent behavior, design intelligent agents, identify AI problems and solve them, design knowledge representation and expert systems, design neural networks for solving problems, identify machine learning paradigms and their practical applications.
Course Contents
Artificial Intelligence (AI) and AI Perspectives: acting and thinking humanly, acting and thinking rationally, History of AI, Foundations of AI, Applications of AI
Introduction of agents, Structure and Properties of Intelligent Agents, Configuration of Agents, PEAS description of Agents, Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based, Environment Types: Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi Agent
Problem as a state space search, Problem formulation, Well-defined problems, Solving Problems by Searching, Search Strategies, Performance evaluation, Uninformed Search: Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search, Informed Search: Greedy Best first search, A* search, Hill Climbing, Simulated Annealing, Game playing, Adversarial search techniques, Mini-max Search, Alpha-Beta Pruning, Constraint Satisfaction Problems
Definition and importance of Knowledge, Issues in Knowledge Representation, Knowledge Representation Systems, Types of Knowledge Representation Systems: Semantic Nets, Frames, Conceptual Dependencies, Scripts, Rule Based Systems, Propositional Logic, Predicate Logic, Propositional Logic: 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, Unification and lifting, Inference using resolution, Handling Uncertain Knowledge: Random Variables, Prior and Posterior Probability, Inference using Full Joint Distribution, Bayes' Rule, Bayesian Networks, Reasoning in Belief Networks, Fuzzy Logic
Introduction to Machine Learning, Concepts of Learning, Supervised, Unsupervised and Reinforcement Learning, Statistical-based Learning: Naive Bayes Model, Learning by Genetic Algorithm, Learning with Neural Networks: Biological vs. Artificial Neural Networks, Mathematical Model of ANN, Types of ANN: Feed-forward, Recurrent, Single Layered, Multi-Layered, Applications, Supervised vs. Unsupervised Learning, Hebbian Learning, Perceptron Learning, Back-propagation Learning
Laboratory Works
Design and implementation of intelligent agents and expert systems,Implementation of searching techniques,Implementation of knowledge representation systems,Implementation of machine learning techniques,Implementation of Neural Networks and Genetic Algorithms using LISP, PROLOG, or any high-level language