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Academy of Engineering Sciences
Computer and electronics Programme
Semester : XIII/ Academic year : 2015 – 2016
COURSE INFORMATION
Course Code:
Credit Hours:
Prerequisite: NONE
Course Title: INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Course Instructor:
Mr. MOHAMMED ABDELGADIR ELSHAIKH, 102 Althora Omdurman – Sudan
Tel: (+249)122069188
e-mail address: [email protected]
URL: http://www.freeteaching.esy.es/
Office Hours: Tue 09:00 -11:00, in the Teachers Room, 3rd. Floor .
Office Hours:
Office hours are the primary mechanism for individual contact with Instructor.
All students are strongly encouraged to make use of office hours.
Lecture:
Sunday: 08:30-10:30 in BASHEER Hall 2nd. Floor,
__
Laboratory : (ON LINE LAB )
URL: http://www.freeteaching.esy.es/
Main Web Page:
http://www.freeteaching.esy.es/myCourses20152016AES/mynew/ai.html
Course Description:
Lecture 1 hour, 2 credits, Artificial Intelligence, Introduction to Neural Networks and
Fuzzy Logic
Prerequisites
None
Textbook
 Bratko I, Prolog Programming for Artificial Intelligence(2nd Edition),
Addison Wesley
 Rich E and Knight K, Artificial Intelligence(2nd Edition),McGraw Hill,
 Copeland J, Artificial Intelligence: A philosophical Introduction Addison
Course Objectives
At the completion of the subject, students should be able to:
 Have a clear understanding of AI definitions, its knowledge
representations, use of logic and structural representations.
 Define the expert system structure, development process, knowledge
acquisition and development tools.
Topics Covered
Artificial Intelligence
 Definition and objectives of AI
 signs of intelligence
 Turing test. Brief history of AI
 Major areas of AI
 Application areas of AI
 AI vs. conventional programming.
 Knowledge representation, use of logic and structural representation.
 Problem representation,
 Problem solving methods.
 AI languages,
 computer architectures for AI applications,
 Signal and image processing. Definition of expert system,
 Structure of an expert system.
 Expert system development process, knowledge acquisition,
development tools).
 Introduction to Neural Networks and Fuzzy Logic
Class/Laboratory Schedule
Two 60-minute Lecture and one 50-minute tutorial sessions per week. One
120 -minute laboratory session per week.
Computer Applications
Prolog or Lisp
Reading Assignments:
Reading assignments include sections of the required textbook, distributed readings,
and supplementary notes handed out in lecture.
Problem Sets:
There will be a number of problem sets. Each new problem set will normally be posted
on the course website. Problem sets should be turned before or in the day that it is due.
Solutions will be posted on the web.
Laboratory:
The laboratory exercises are intended to reinforce the material covered in lecture and in
problem sets.
For this course we are going to connect to an ONLINE LAB - - It is expected that
students will work individually.
Handouts will be distributed for each lab experiment.
Lab reports are required from each.
You should use lab experiments and the reports as a means to understand the topic.
Midterms:
In this course there will be three midterm exams .
Final Exam:
The final exam will be comprehensive, covering all of the material in the course. This
includes everything covered in problem sets, lectures, and readings. The exam will be
held during the Examination Period at the scheduled time.
Computer Account:
You will receive an ID number and password on a document handed out in class. This
account will give you access to your course work and grades.
Expected Performance and grading of your work:
To give you as much information about your expect performance and the grading of
your course work, GRADING RUBRICs will be posted on the course web page.
Delivery Methods
The methods of instruction for any this course will include:
√
Lectures
√
Group Discussion
√
Tutorials
√
Laboratories (Virtual Online LAB)
Visits
x
Modes of Assessments
Assessment will be done through continuous Interim course work [Assignments ; Midterm Exams ; Course Project ; Seminar ] and a final examination.
Interim assessment will carry a total of 40% and final examination will carry 60% of
the final grade mark.
Grading Policy:
Course grades will be assigned according to the following grading :Problem Sets:
Laboratory/Projects:
Midterm Exam:
Final Exam:
05%
20%
15%
60%