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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

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)* Face to face: 28
Guided learning: 16
Credit* Independent learning: 31
Assessment: 5

Course Learning Outcomes* At the end of the course, students are able to:

1. Describe the Principals of Computational Intelligence methods such as
neural networks and population based metaheuristic algorithms
Neural networks models
The Perceptron model
Introduction to metaheuristic search and optimization
2. Apply multi-layer perceptron back-propagation neural networks
3. Apply genetic algorithms for solving optimization problems

Transferable Skills None

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)

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