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CSE 860 Artificial Intelligence
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Campus
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SMME
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Programs
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PG
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Session
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Fall Semester 2016
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Course Title
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Artificial Intelligence
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Course Code
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CSE 860
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Credit Hours
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3-0
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Pre-Requisutes
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Course Objectives
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The primary objective of this course is to provide an introduction to the basic principles and applications of Artificial Intelligence. Programming assignments are used to help clarify basic concepts. The emphasis of the course is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments. In short, this is course is about the design and implementation of intelligent agents---software or hardware entities that perform useful tasks with some degree of autonomy.
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Detail Content
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Introduction to AI
Definitions ( Acting Humanly, Cognitive, “laws of Thought”, Rational agent
approaches
Historical perspective
Physical symbol system hypothesis
Intelligent agents
Agents and Environment
The concept of rationality
Performance measures
Omniscience, learning and autonomy
Nature of environments, Task environments and their Properties
The Structure of agents
Simple Reflex agent
Model based agent
Goal Based agents
Utility based agents
Learning agents
Problem solving by searches
Problem solving agents
Problem formulating
Measuring performance
Search Strategies
Uninformed Searches
Breadth first
Depth first
Depth limited
Iterative deepening depth-first
Bidirectional
Comparison of Uninformed problem solving methods
Informed searches
Greedy best-first
A*
Heuristic Functions ( learning, devising)
Local search algorithms
Hill climbing
Simulating annealing
Local beam
Genetic algorithms
Constraints Satisfaction problems
Basics of CSP
Structure of problems
Backtracking, Forward chaining
Variable and value ordering
Intelligent backtracking
Local searches for CSPs
Adversarial Searches
Games
Optimal decisions
Mini-max algorithm
Alpha-Beta pruning
Logical agents
Knowledge-based agents
Propositional logic (syntax, semantics)
Conjunctive/disjunctive Normal form, Horn clauses
Reasoning in Propositional logic
Resolution
Forward and backward chaining
Reasoning algorithms
First-Order Logic (syntax, semantics)
Inference in FOL
Conjunctive Normal form
Unification and lifting, forward and backward chaining, resolution
Comparison of two representational languages
Machine Learning
Introduction ,induction, Types of machine learning
Nearest Neighbors
Decision Trees
Neural Networks
Learning Conjunctions
Linear and non Linearsaparability
Evaluating learning algorithms
Natural language Understanding
Levels of language analysis
Types of grammar
Parsing(Top-down, bottom-up)
natural language applications.
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Text/Ref Books
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[AIMA] Artificial Intelligence: A Modern Approach (2nd Edition), by Stuart Russel and Peter Norvig, Prentice Hall, 2002
ISBN-10: 0137903952, ISBN-13: 978-0137903955
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Time Schedule
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Fall Semester 2014
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Faculty/Resource Person
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Dr Yasar Ayaz
PhD (Tohoku University) Japan
Discipline: Mechatronics Engineering
Specialisation: Robotics & Machine Intelligence
Dr Irtiza Ali
PhD (Georgia Institute of Technology) USA
Discipline: Aerospace Engineering
Specialization: Aerial Robotics
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