Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Computational Intelligence | |||
| Reference | CM4607 | Version | 1 |
| Created | February 2024 | SCQF Level | SCQF 10 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| 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 to 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. 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 | 48 | N/A |
| Non-Contact Hours | 102 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 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: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Individual Coursework covering all learning outcomes. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| 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. |