banner
Theory :
Lab :
   ~ Back to Taif University on NOV 2012!      ~ Publications, Patents, Databases, Software Engineering, Graphics, Image Processing, Cisco Training, Lecture Notes are updated!      ~ E-Journals, Software Engineering, Cisco Training, Everyday English, and Album Sections have been updated!      ~ This website will be updated gradually since I am busy with my master project      ~ Welcome to u2learn.net... All my students are invited!      
Neural Networks (501422-3)

 Course hashtag #501422NN

Announcements, Mid-Term Marks, Answer Model

Read more announcements here!

Student Feedback and Appreciation of Lectures (download Excel file)

An artificial neural network, often just called a neural network, is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases a neural network is an adaptive system that changes its structure during a learning phase. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data.. (See Wikipedia)

~Course Information:

    Course syllabus - student version

Code

501422-3

Title

Neural Networks

Credit Hrs.

3-0:3

Semester

2

Academic Year

2012-2013

Pre/Co Requisites

2501361-3 Artificial Intelligence

Instructor

Office Hours/ Location

Email

Teaching Assistants

Dr. Salha M. Alzahrani

Mon. 8am-12pm
Tues. 8am-12pm
Garwa deanship block, Room 1630

admin@c2learn.com
s.zahrani@tu.edu.sa

Please send your enquiries to one email address not both!

Mrs. Samsad
Mrs. Ameera Almas

Course Format

Lecture Time/Venue

Course Website

Lectures: 28/Section

Lab: 28/Section

Section 266 (Sat. 8am-10am) @22
Section 212 (Sun. 8am-10am)  @25
Section 243 (Sun. 10am-12pm)  @21

www.c2learn.com/neural_networks/

~Synopsis:

This is an introductory course to artificial neural networks (ANNs). The course starts with a descriptive analogy with biological neural networks. The evolution of the main models of ANNs is introduced. In particular, McCulloch and Pitt's Model is discussed through which the basic concepts of ANNs are explained. The perceptron model is detailed and the linear separability concept is discussed and shown to be the reason behind the decline of ANNs in the late 70s. Single-layer versus multi-layer feed-forward neural networks are discussed with hands-on exercises. Back-propagation model, as a solution to the limitation of perceptron learning, is introduced and the learning algorithm is discussed in depth. Additional ANNs models, as the unsupervised Self-Organizing Maps, are introduced as well.

~Course Objectives:

CO1 : Introduce the main fundamental concepts and techniques of artificial neural network models.
CO2 : Investigate the main artificial neural network models and their applications. .

~Learning Outcomes:

Upon successful completion of the course, the students will be able to:
CLO1 : Understand and describe the analogy between biological and artificial neuron.
CLO2 : Understand how early neural network systems carried out computations.
CLO3 : Understand the learning process of Perceptrons models.
CLO4 : Understand the linear separability of perceptron models and how it restricts its learnability.
CLO5 : Use the back-propagation algorithm to solve the restrictive learning problem in perceptrons.
CLO6 : Use an unsupervised learning to train models based on self-organized maps.
CLO7 : Evaluate the practical considerations in applying neural networks in real classification problems. .

~Course Schedule:

Week

Topic

Slides

Assignment(s)/
Lab Exercise(s)

Quiz(zes)

0
26/1
/2013

Course syllabus and structure Lecture_0    

1
2/2/2013

Introduction to Biological and Artificial Neural Networks (History and Evolution)

Lecture_1

Discussion of research works on neural networks. Sample of a paper entitled “Review of neural network applications in sleep research”, Journal of Neuroscience Methods, Elsevier.

Introduction to MATLAB environment and the Neural Networks Toolbox.

 

2
9/2/2013

The Neuron as a Simple Computing Element: McCulloch–Pitts (MCP) Neuron

Lecture_2

More on MATLAB environment and the Neural Networks Toolbox.

 

3
16/2/2013

Perceptrons I: Linear Separability Concept and Learning Algorithm

Lecture_3

NN_Handout_Perceptron_AND

NN_Handout_Perceptron_OR

Using the Neural Networks Toolbox and Wizard with AND, OR, and Irish datasets.

 

4
23/2/2013

Hands-on Exercises of Perceptrons Learning: Logical AND, OR, NAND

Single-layer and Multilayer Feed Forward Neural Networks

Lecture_4

NN_Handout_Perceptron_AND_3Inputs

NN_Handout_Perceptron_OR_3Inputs

NN_Handout_Perceptron_NAND_3Inputs

Exercise(s) and practical problems on perceptrons.

5
2/3/2013

More Hands-on Exercises of Perceptrons Learning

Lecture_5

NN_Handout_Single_Layer_Feedforward

NN_Handout_MultiLayer_Feedforward (3-2-2-1)

Exercise(s) and practical problems on perceptrons.

 Quiz I-A

Quiz I-B

Quiz I-C

6
9/3/2013

Multilayer Neural Networks and MATLAB Demos.

Lecture_6

NN_Handout_MultiLayer_Feedforward (2-3-2)

Exercise(s) and practical problems on single-layer versus multi-layer feed-forward neural networks.

7
16/3//2013

Mid Semester Exams

 ---

 

 

-
23/3//2013

Mid Semester BREAK

 ---

 

 

8

Back-propagation Learning Algorithm I

Lecture_7

Exercise(s) and practical problems on back-propagation neural networks.

 

9

Back-propagation Learning Algorithm II

Lecture_8

NN_Handout_BP_XOR

NN_Handout_BP_Bipolar_XOR

NN_Handout_BP_ZIP (.doc files)

Assignment 2

Exercise(s) and practical problems on back-propagation neural networks.

 Solution of BP_XOR (.doc)

10

Accelerated learning in multilayer neural networks.

Lecture_9

MATLAB Demos

Exercise(s) and practical problems on back-propagation neural networks.

Assignment 2 due L

 Quiz II: will be held during your lab

11

Back-propagation Learning Algorithm: Example of a Real Classification Problems.

Lecture_10

Exercise(s) and practical problems on back-propagation neural networks.

 

12

Unsupervised Learning using Self-Organizing Maps (SOMs)

---

Exercise(s) and practical problems on back-propagation neural networks.

 Quiz III A&B

13

Review of the Course

---

Lab Revision.

 

14

Lab Final Examination

 

 

15&16

Final Exam

---

 

 

 

Important Downloads:

Academic vocabulary list 
Neural networks glossary.
Khan, A. H. (1996). Feed-forward neural networks with constraint weights. University of Warwick.

~Course Assessment:

Quizzes: 10, Assignment(s)/Project(s): 5, Lab: 15, Mid Term Exam: 20, Final Exam 50.

~Text/References:

Main Textbook:
Sivanandam, M. Introduction to Artificial Neural Networks, Sangam Books Ltd., 2003. ISBN-10: 8125914250.  

Other Reference(s):
Fausett, L., Fundamentals of Neural Networks, Prentice Hall, 1994.

Negnevitsky, M., Neural Networks in Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Addison Wesley, Harlow, England, 2005.

 

~Course Policies:

Attendance

it is compulsory for students to attend at least 75% of the whole time allocated to the course. More than four (4) unjustified absences will make you ineligible for the course.

Quizzes

students are informed about the date of their quizzes. Check the above course schedule to see when a quiz is due.

Assignments

students are expected to submit their assignments in a timely manner.

Late Submissions

late submission may be subjected to a decrease in the grade that is proportional to the delayed days.

Mid-terms and Finals

exams are closed books. Students who did not attend the mid-term due to exceptional situations (e.g., medical emergency) will see their mid-term result calculated from their final. 0.75 * Final exam mark will be used as their mid-term result.

Plagiarism

student caught cheating will be awarded a 0 in the exam and may be subject to university disciplinary actions.

Disability

students with disability need to approach the course instructor so that commodities will be put in place to assist them. This is done in a personal and confidential manner.

 

 


:: Home :: Lecture notes :: Calendar :: E-Learning :: Contact us :: Guest book :: Taif University :: CIT ::

All rights reserved for © u2learn.net 2008-2012

For best resolution use: 1024 x 768
You are using :