Course
BCS3253 - ARTIFICIAL INTELLIGENCE
DOWNLOAD:
Module 1 : Intro to AI
Lecture1
Module 2: Expert System
Lecture1
Lab1
Module 3: Fuzzy Logic
Lecture1
Lecture2
Assignment
Module 4: Neural Networks
Lecture1
Lecture2
Lecture3
BP Calculate
Module 5: Genetic Algorithms
Lecture1
SINOPSIS:
This course introduces student to the theory and practice of the Artificial Intelligence (AI). Student are expose to the main artificial intelligence concept currently most applied in application such as Artificial Neural Networks(ANN), Fuzzy Logics(FL), Genetic Algorithms(GA) and Expert Systems(ES). Practical examples of how artificial intelligence is applied to commercial, scientific, federal and consumer applications will be covered.
HASIL PEMBELAJARAN:
Upon the completion of this course, students will have the ability to:
1. Apply artificial intelligence concept in computer sciences
2. Apply AI tools and method in computer applications
3. Produce intelligence system prototype/module
KEMAHIRAN GENERIK:
At the end of the course, the students should have acquired the various generic skills targeted as below:
1. Learning capabilities: Able to research, explore, learn and use new AI tools and method for computer applications
2. Problem solving: Able to choose suitable AI tool and method to solve a given problem
3. Team working/ Communication skills: Able to work effectively in a team and present the team decision/solution for a given problem
KAEDAH PENGAJARAN:
Lectures, laboratory exercises, group projects, group assignments
SILIBUS:
1 Chapter 1: Introduction to AI
1.1 What is AI
1.2 Foundation of AI
1.3 The AI Problem
2 Chapter 2 : Rule Base Expert System
2.1 Overview of ES
2.2 ES Architecture & System
2.3 Backward Chaining ES
2.4 Forward Chaining ES
3 Chapter 3 : Fuzzy Logic
3.1 Overview of FL
3.2 Fuzzy Set
3.3 Fuzzy Operation
3.4 Fuzzy Inference
4 Chapter 4 : Artificial Neural Network
4.1 Overview of ANN
4.2 ANN Structure & Learning
4.3 Hebb Rule
4.4 Perceptron
4.5 Multi Layered ANN
5 Chapter 5 : Genetic Algorithm
5.1 Overview of GA
5.2 Evolutionary Computation
5.3 Representation
5.4 Fitness Function
5.5 Genetic Operator
6 Chapter 6 : AI in Data Mining and Knowledge Discovery
6.1 Overview of DM & KDD
6.2 DMM Method
6.3 DMM Technique
PENILAIAN:
Item Percentage (%)
Quizes 10
Assignment 10
Mini Project 20
Mid-Term Test 20
Final Exam 40
Total 100
RUJUKAN:
1. Negnevitsky, M. 2004. Artificial Intelligence A Guide to Intelligent Systems 2nd Edition. Essex, London: Pearson Education Limited
2. Russell, S.J. & Norvig, P. 2003. Artificial Intelligence A Modern Approach: International Edition 2nd Edition. New Jersey: Pearson Education Inc
3. Thuraisingham, B. 2000. A primer for understanding and applying data mining. IT Professional 2(1): 28 - 31
