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COURSE PRO FORMA

IMPORTANT:
Contents of this Pro Forma shall not be changed without the Senate’s approval for items indicated with *. Changes
to the other items can be approved at the Academy/Faculty/Institution/Centre level.

Academy/Faculty/Institute/Centre Engineering
Department Mechanical Engineering

Programme Bachelor of Engineering (Computer Aided Design and Manufacturing)

Course Code* KCEP 4310
Course Title* Computational Intelligence for Engineering and Manufacture

Course Pre-requisite(s)/ Minimum Require- none
ment(s)

Student Learning Time (SLT)* 120
Credit* 3

Course Learning Outcomes* Matlab tools: 1- Neural network toolbox; 2- Optimization toolbox

Transferable Skills At the end of the course, students are able to:
1. Describe the elements and principles of computational Intelligence
method, neural networks and genetic algorithm for search and optimi-
zation
2. Apply multi-layer perceptron back-propagation neural networks

3. Apply genetic algorithms for solving optimization problems

Synopsis of Course Contents The aim of this course is to offer the fundamentals of some computational
intelligence methods such as neural networks and genetic algorithm. The
course introduces neural networks models with the emphasis on the multi-
layer perceptron used for classification and predictions. The fundamental
concepts of optimization and search in engineering are introduced. The
course also introduces the concepts and application of genetic algorithms.

Learning Strategies (lecture, tutorial, work- Lectures, Lab
shop, discussion, etc)
Assessment Weightage* Continuous assessment : 40%
Final examination: 60%

Methodologies for Feedback on Perfor- Discussions in class
mance Returning graded assignments and tests
Final grades are announced

Refer to the University of Malaya (First Degree) Rules 2013 and the Uni-
Criteria in Summative Assessment versity of Malaya (First Degree) Regulations 2013.

UM-PT01-PK03-BR003(BI)-S04