COMPUTATIONAL INTELLIGENCE 2/2014/2015

2014/2015

SEMESTER 2

COMPUTATIONAL INTELLIGENCE

Lecturer : PM Dr Siti Zaiton Mohd Hashim
Room No. : Academic Office, Level 3, N28a
Telephone No. : 0197726248
E-mail : sitizaiton@utm.my
Class Hours : Computer Lab, Level 2, N28a
Monday (11.00 a.m. – 12.00 p.m.) – Section 02;
Tuesday (02.00 p.m. – 03.00 p.m.) – Section 01;Lecture Room 7 & 8, Level 1
Friday (08.15 a.m. – 10.15 a.m.) – Section 01 & 02
Synopsis : The aim of this course is to expose the students current methods and algorithms utilized in specialized areas of artificial intelligence. The methods include knowledge representation of vague data and inferences using fuzzy logic, learning using neural network and searching using evolutionary algorithms. Students will be equipped with the theories and the necessary skills to model the domain problems suited to the associated techniques or algorithms. This course will cover the topics on fuzzy logic, neural network and evolutionary algorithms.
Learning Outcome : At the end of the course, students should be able to :

  • Describe the basic concepts of computational intelligence (CI) techniques such as fuzzy logic, neural network and evolutionary algorithms.
  • Identify appropriate CI technique in solving domain’s problem.
  • Work in a team to accomplish CI-based solution in problem solving.
Reminder :
  • ATTENDANCE is compulsory. Students with less than 80% attendance will be prohibited from sitting the final exam.
  • Students MUST DRESS respectably (SOPAN in malay) when attending the lecture, please follow the dress code required.
  • Students must abide by the rules set by University at all times in the lectures, laboratories and exams.
  • CHEATING in any form is PROHIBITED. Gred F is automatically given if the student is caught cheating during the exam. Zero mark is given if the student is found copying/plagiarizing other’s work.
  • Quizzes and EXAMs ARE NOT REDONE unless the student is sick and certified by UTM Medical panels, to be done within a week after the exam.
  • LATE assignment will be penalized or rejected.
Course Assessment : Quiz 15% (3 out of 4 will be selected)
Assignment 15% (3)
Paper Review & Presentation 15% (1)
MidTerm Exam 15% (1)
Final Exam 40% (1)
 

Week
Topics
Activities/hours
 –
SEM I (7 WEEKS)
  –
Week 1
 1.0 Introduction to Computational Intelligence:From Biology to Industry
Lecture 1
Lecture 2
Discussion on Project Paper Review
Week 2 2.0 Fuzzy Logic
2.1  Introduction: what is fuzzy thinking?
2.2  Fuzzy Sets
2.3  Liguistic variables and hedges
2.4  Hands On

 Lecture 1
Instruction for Group Project -Malay
Instruction for Group Project- English
IEEE Explorer
Science Direct
Confirm selected Journals for Paper Review
Week 3
 3.0 Fuzzy Logic
3.1  Operations of fuzzy sets
3.2  Fuzzy Rules
3.3  Hands On		 Lecture 2
Exercise Fuzzy TipWaiter
Example Problem: Dinner for Two
Hands on Matlab
Quiz I
Week 4  4.0 Fuzzy Logic
4.1  Fuzzy Inference
4.2  Sugeno-model
4.3  Mamdani-model
4.4  Hands On
 Example Review Paper
Week 5 5.0 Fuzzy Logic
5.1  Building Fuzzy System
5.2  Hands On
 Neural Network Lecture
HandsOn NN
HandsOn NN_2
Assignment 1 – Fuzzy Logic
Assignment 2- Neural Network
Hands on NN_BP_RubberProduction
Assignment 1
Submit 1st draf Paper Review
Week 6 6.0 Neural Network
6.1  Introduction, or how the brain work
6.2  The neuron as a simple computing element		 Lecture 1
Submit Program Assignment I
Week 7 7.0 Neural Network
7.1  The peceptron
7.2  The Multilayer neural networks
7.3  Hands On		 Lecture
Return 1st draft Paper Review with comments
Week  8  Sem Break  –
 –

SEM II (7 WEEKS)

  –
Week  9
 9.0 Neural Network
9.1  Hidden Layer
9.2  Back Propagation
9.3  Hands On
 Lecture
MIDTERM EXAM
Week  10 10.0 Neural Network
10.1  Multilayer neural networks (cont..)
10.2  Hands On		 Lecture
Quiz III
Week 111
 11.0 Genetic Algorithms
11.1  Introduction
11.2  Simulation of Natural Evolution		 Lecture 1
Format and Tips for Paper Writing
Hands on GA1
Example of Review NN in SleepResearch
Example of Review in Image Processing
Example of Review in Soft computing Engineering Design
Quiz II
Submit Program Assignment
Submit 2nd draft Paper Review
Week 12
 12.0 Genetic Algorithms
12.1  Genetic Algorithms:Mice and Cat
12.2  Example 1:Burger and Profit Problem
12.3  Example 2:Optimization of simple equations
12.4  Hands On		 Lecture 2
Exercise GA TSP
Example of GA_2 Variable Problem
Week 13 13.0 Genetic Algorithms
13.1  Example 3:Optimization of complex equation
13.2  Example 4:The traveling Salesman Problem
13.3  Hands On		  –
Week 14
 13.0 Genetic Algorithms
13.1   Example 4:The traveling Salesman Problem (cont..)
13.3  Hands On
13.2  Group Presentation		 Review Lecture
Review Return 2nd draft Paper Quiz II
Submit Program Assignment III Paper Review Presentation
Week 15 GROUP PRESENTATION
 Paper Review Presentation
Week 16 STUDY WEEK
 Submit Corrected Paper Review
Week 17 FINAL EXAMS (40%)

 

Text/Reference:

  • Deyi, L. and Yi, O., Artificial Intelligence With Uncertainty, Chapman & Hall/CRC, 2008.
  • Negnevitsky, M, , Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Addison Wesley, Harlow, England, 2005.
  • Tim Jones, M., AI Application Programming, Charles River Media, Massachusetts, 2003.
  • Poole, D., Mackworth, A. and Goebel, R., Computational Intelligence A Logical Approach, Oxford University Press, 1998.
  • Pedrycz, W., Computational Intelligence: An Introduction, CRC Press, 1997.
  • Michell, M., An Introduction to Genetic Algorithms (Complex Adaptive Systems), MIT Press, 1998.
  • Haykin, S.S., Neural Networks: A Comprehensive Foundation, 2nd. Ed., Prentice Hall, 1998.
  • Tanaka, K., An Introduction to Fuzzy Logic for Practical Applications, Springer-Verlag, 1998.