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2019/20 Taught Postgraduate Module Catalogue
COMP5400M Bio-Inspired Computing
15 creditsClass Size: 120
Module manager: Marc de Kamps
Email: m.dekamps@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2019/20
This module is not approved as an Elective
Module summary
Consider examples of cooperative phenomena in nature and the concepts of emergence and self-organisation. Design and apply simple genetic algorithms.Interpret the behaviour of algorithms based on the cooperative behaviour of distributed agents with no, or little, central control.Implement bio-inspired algorithms to solve a range of problems.Objectives
On completion of this module, students should be able to:- understand how natural computing and conventional AI can complement each other;
- understand algorithms that are based on cooperative behaviour of distributed systems with no, or little central control;
- understand, design and apply simple genetic algorithms;
- understand the relation between artificial neural networks and statistical learning;
- understand how the fields of artificial neural networks and computational and cognitive neuroscience inform each other;
- read and discuss recent research papers in selected journals and conferences and give a presentation on a recent topic in bio-inspired computing.
Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-to demonstrate in-depth, specialist knowledge and mastery of techniques relevant to the discipline and/or to demonstrate a sophisticated understanding of concepts, information and techniques at the forefront of the discipline;
-to exhibit mastery in the exercise of generic and subject-specific intellectual abilities;
-to demonstrate a comprehensive understanding of techniques applicable to their own research or advanced scholarship;
-proactively to formulate ideas and hypotheses and to develop, implement and execute plans by which to evaluate these;
-critically and creatively to evaluate current issues, research and advanced scholarship in the discipline.
Syllabus
- Examples of cooperative phenomena in nature.
- Concepts such as emergence, self-organisation and embodiment.
- Genetic algorithms.
- Algorithms for swarm intelligence.
- Biological neural networks.
- Various artificial neural networks and their application (eg, clustering, dimensionality reduction).
- Models in computational and cognitive neuroscience.
- Models of biological computation in computational/cognitive neuroscience and/or bioinformatics.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Class tests, exams and assessment | 1 | 2.00 | 2.00 |
| Lecture | 22 | 1.00 | 22.00 |
| Private study hours | 126.00 | ||
| Total Contact hours | 24.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Private study
- Taught session prep: 22 hours.- Taught session follow-up: 44 hours.
- Self-directed study: 25 hours.
- Assessment activities: 35 hours.
Opportunities for Formative Feedback
Attendance and formative assessment.Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Lab Work 2 | 15.00 |
| Assignment | Lab Work 1 | 15.00 |
| Assignment | Lab Work 3 | 10.00 |
| Total percentage (Assessment Coursework) | 40.00 | |
This module is re-assessed by exam only.
Exams
| Exam type | Exam duration | % of formal assessment |
| Open Book exam | 2 hr 00 mins | 60.00 |
| Total percentage (Assessment Exams) | 60.00 | |
This module is re-assessed by exam only.
Reading list
There is no reading list for this moduleLast updated: 30/04/2019
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- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
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