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 module

Last updated: 30/04/2019

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