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Guidelines_English_Final
تحميل الدليل التدريبي

أسئلة شائعة


Artificial Intelligence                 الذكاء الاصطناعى

CSC361

Spring 2011

 

Project Grading (Phase 1)

Game rules for the project (New)

Course outline

Project Deliverables,Semester Schedule and Rubrics

Attendence & Bonus: Any discripencies send an e-mail to basit@ksu.edu.sa

Course Code: CSC 361

Course Title: Artificial Intelligence

Credit Hours:   3(3,0,1)

 Lecture Time: 1:0 - 3:0 Sunday  &  1:0 - 3:0 Tuesday

Pre-requisites: CSC 212

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.

Course Objective:

The objective of the course is to enable students to learn about the tools and techniques for understanding and applying their knowledge to solve real world problems. The course will focus on sharpening the practical skills of the students alongwith providing them the theoratical knowledge.

 

  Syllabus (Course in Progress)

   

Week

date

Week #

Chapter

Notes

 

Due Date

 

 

 

 

1

Introduction

 

 

2

Problem Search Solving

 

 

 

   3

4

Uninformed Search

 

 Project Phase-1

 08/03/2011

   5

6

Informed Search

 

 Project Phase-2

 29/03/2011

 

 7

 

 

 HW-1   1-4-2011

17-4-2011 

   8

Game Playing 

  9 Dersarial Search and Game Playing examples MidTerm-1 12-04-2011
  10 Project Phase-3 19-04-2011

11

Optimization

 

 

 

 

 12

 

 

Project Phase-4 

 03/05/2011

 13

Constraint Satisfaction Problems (CSP)

 

 HW-2       08-05-2011

 15-05-2011

14

 

 Midterm-2   15-05-2011

 

 

15 

 Knowledge Representation

 Project Phase-5

 19-05-2011

16

First Order Logic

 

 

 

 Recommended Books

1.      “Artificial Intelligence: A modern approach

Stuart Russell, Peter Norvig, Prentice Hall, 2003 (new edition 2006)

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

   

Exam

Score Ratio

Time

Place

MedTirm1

15%

CCIS Building,   Room # 30

MedTirm2

15%

CCIS Building,   Room # 30

Final exam

40%

 

 

Homework, Quizzes, Project

30%

CCIS Building,   Room # 30

 

Tutorials & Project (Previous Semesters-2010)

1. Course project

2. Problem Formulation: Assignment, Solution

3. Blind Search : Assignment, Solution

4. Heuristic Search : Assignment, Solution, Assignment2

5. Optimization: Assignment, Solution

6. Constraint Satisfaction Problems: Assignment, Solution

7. Game Playing 

8. Knowledge Representation & Expert System: Assignment, Solution

9. Prolog language and Samples

10. Machine Learning: Assignment, Solution

 Homework (Previous Semester-2010)

1- Record Blog. , and eCorrect site Service

2- Assignment2 

3- Assignment3

4-Assignment4

5-Assignment5

6-Assignment6

Exams - Solutions 

 

Old Exams

  • Midterm-1 (Question Paper and Solution) Part1    Part2  (Nov 2010)
  • Midterm-2 (Question Paper and Solution) part1   part2  (Dec 2010)
  • FINAL EXAM: Exam - Solution 

         MIDTERM I: Exam - Solution

  • FINAL EXAM: Exam - Solution 

 Office Hours

        Monday                   9:00 am ------  11:00 am

       Tuesday                   9:00 am -------  10:00 am

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)

 

 

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