This Module Version is not active until 01/Sep/2024
MODULE DESCRIPTOR |
Module Title |
Computational Intelligence |
Reference |
CM4601 |
Version |
2 |
Created |
March 2024 |
SCQF Level |
SCQF 10 |
Approved |
July 2020 |
SCQF Points |
30 |
Amended |
April 2024 |
ECTS Points |
15 |
Aims of Module |
To provide key concepts of adaptive intelligent systems and the design and implementation of such systems for real-world problems. |
Learning Outcomes for Module |
On completion of this module, students are expected to be able to: |
1 |
Examine problems in optimisation and machine learning and select suitable solution techniques. |
2 |
Develop an adaptive intelligent system for a given application. |
3 |
Illustrate current application areas of adaptive intelligent systems. |
4 |
Execute selected computational intelligence algorithms in adaptive intelligent systems. |
Indicative Module Content |
Techniques: evolutionary algorithms (GA, EDA, PSO, ACO), local search, constraint satisfaction and optimisation. Applications: function optimisation, artificial life, network analysis, biology and medicine, neural networks, image analysis, engineering, evolutionary art and music and parameter tuning.
Theory: exploration v exploitation, local and global optima, satisfaction and optimisation, premature convergence, plateauing and Schema Theorem.
Practical: problem representations, selection, genetic operators, parameter choices, evaluation and tuning of algorithms, toolkits and real world case studies in scientific optimisation, medicine, engineering and industry. |
Module Delivery |
Key concepts are introduced and illustrated through the medium of lectures. These are reinforced in tutorial classes. Laboratory sessions provide a series of exercises designed to develop proficiency in techniques essential to the development of adaptive intelligent systems. |
Indicative Student Workload |
Full Time |
Part Time |
Contact Hours |
96
|
N/A
|
Non-Contact Hours |
204
|
N/A
|
Placement/Work-Based Learning Experience [Notional] Hours |
N/A
|
N/A
|
TOTAL |
300
|
N/A
|
Actual Placement hours for professional, statutory or regulatory body |
 
|
 
|
ASSESSMENT PLAN |
If a major/minor model is used and box is ticked, % weightings below are indicative only. |
Component 1 |
Type: |
Examination |
Weighting: |
50% |
Outcomes Assessed: |
1, 3
|
Description: |
Closed book Examination. |
Component 2 |
Type: |
Coursework |
Weighting: |
50% |
Outcomes Assessed: |
2, 4
|
Description: |
Individual Coursework. |
Module Requirements |
Prerequisites for Module |
CM2601 and CM1606 or equivalents. |
Corequisites for module |
None. |
Precluded Modules |
None. |
INDICATIVE BIBLIOGRAPHY |
1 |
Kordon, A. 2010. Applying Computational Intelligence: How To Create Value. Springer. |
2 |
Kacprzyk J, Pedrycz W (2015) Springer Handbook of Computational Intelligence. |
3 |
Fogel D, Liu D, Keller J (2016) Fundamentals of Computational Intelligence: Neural Networks, Fuzzy
Systems, and Evolutionary Computation. |
4 |
Bhattacharyya, Snášel, Pan, Debashis (2019) Hybrid Computational Intelligence: Research
and Applications. |
5 |
Kumar, Raman, Wiil (2019) Recent Advances in Computational Intelligence. |
6 |
Hemanth , Jude (2019) Human Behaviour Analysis Using Intelligent Systems. |