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Guidelines_English_Final
تحميل الدليل التدريبي

أسئلة شائعة


 


  Artificial Intelligence

(CSC 361)


Students should check these pages at least once a week for important information.

Course Description

This course provides a general introduction to AI (Artificial Intelligence): Its techniques and its main sub-fields. It gives an overview of underlying ideas, such as search, knowledge representation, expert systems and learning.

Recommended Books

1.      Artificial Intelligence: A modern approach”, 3/E

Stuart Russell, Peter Norvig, Prentice Hall, 2010 

2. "Artificial Intelligence - Structures and Strategies for Complex Problem Solving", George F. Luger, Pearson Internationl Edition, 2009

3.      Artificial Intelligence Illuminated

Ben Coppin, Jones and Bartlett Publishers, 2004

 

4.      “Artificial Intelligence: A new synthesis”

Nils Nilsson, Morgan Kaufmann, 1998

5.      “Introduction to expert systems”

Peter Jackson, Addison Wesley, 1999

6.      Lecture slides and your lecture notes!

 

Grading

MT1                                     20%              

MT2                                     20%               

Final exam                             40%               

Homework, Quizzes, Project    20%

 

Detailed Syllabus

            Chapter 1       Introduction

            Chapter 2        Problem Solving

                                   2.1 Search

                                     - Problem formulation

                                     - Uninformed Search (BFS, DFS, DLS, IDS, UCS)

                                     - Informed Search (Greedy Search, A*, IDA*)

                                     - Optimization (Hill Climbing,TS, SA, BS, GA, GP)

                                   2.2 Constraint Satisfaction Problems (CSP)

            Chapter 3        Knowledge Representation

                                 3.1 knowledge representation and reasoning

                                 3.2 Propositional Logic

                                  3.3 First Order Logic

            Chapter 4        Expert Systems & Prolog

                                   4.1 Introduction to Expert System

                                   4.2 prolog

            Chapter 5        Learning (Decision Trees, ANN, Reinforcement Learning)

                                   5.1Introduction to Neural Networks

            Chapter 6        Game Playing

 

Assignments

1. Problem Formulation: Assignment, Solution

2. Blind Search : Assignment, Solution

3. Heuristic Search : Assignment, Solution

4. Optimization: Assignment, Solution

5. Constraint Satisfaction Problems: Assignment, Solution

6. Knowledge Representation & Expert System: Assignment, Solution

7. Machine Learning: Assignment, Solution

8. Game Playing

 

Exams - Solutions (Samples)

 

Selected Topics for Presentation

  • Problem Solving Using AI Techniques: TSP, N-Queens, 8-Puzzle ... 
  • AI and Robotics
  • Computers playing games (Chess, Checkers, BackGammon ...)
  • Swarm Intelligence and Problem Solving
  • Artificial Immune System and Security
  • Datamining (knowledge Discovery)

 

Additional Information

 

 
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