Module manager: Prof Netta Cohen
Email: N.Cohen@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
This module is not approved as a discovery module
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.
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.
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.
- 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.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Private study hours | 128 | ||
| Total Contact hours | 22 | ||
| Total hours (100hr per 10 credits) | 150 | ||
- Taught session prep: 22 hours.
- Taught session follow-up: 44 hours.
- Self-directed study: 27 hours.
- Assessment activities: 35 hours.
Attendance and formative assessment.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Coursework 1 | 40 |
| In-course Assessment | Coursework 2 | 60 |
| Total percentage (Assessment Coursework) | 100 | |
This module is re-assessed by coursework only.
There is no reading list for this module
Last updated: 9/25/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team