Course co-ordinator(s): Dr Michael Lones (Edinburgh), Dr Wei Pang (Edinburgh), Assoc Prof. Hadj Batatia (Dubai), Mohamed Serry (Dubai).
Aims:
Traditional computation finds it either difficult or impossible to perform a certain key range of tasks associated with pattern recognition, problem solving and autonomous intelligence. Great progress towards designing software for such tasks has emerged by taking inspiration from a range of natural, mainly biological, systems.
The aims of this course are to:
- introduce an appreciation of the former
- introduce the main biologically-inspired algorithms and techniques which are now commonly researched and applied
- establish a practical understanding of the real-world problems to which these techniques may be fruitfully be applied.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or equivalent.
Location: Dubai, Edinburgh, Malaysia.
Semester: 1.
Syllabus:
- Classical vs. Biologically-inspired computation,
- evolutionary algorithms (basic EA design, and how they are applied to a wide range of problems)
- swarm intelligence (ant colony methods, particle swarm optimisation)
- neural computation (perceptrons, multilayer perceptrons, associative networks)
- cellular automata
Learning Outcomes: Subject Mastery
Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)
- Understanding of limitations of traditional computation.
- A critical understanding of the two most common biologically inspired computation methods, their limitations and areas of applicability.
- Understanding of how to apply one or more biologically inspired techniques in solving a practical problem.
Learning Outcomes: Personal Abilities
ndustrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
- Identify approaches that can be used to apply bio-inspired methods to existing problems in optimisation and machine learning.
- Exercise reasonable levels of initiative in working with a bio-inspired method (courseworks) (PDP)
- Demonstrate a degree of critical reflection in assessing the performance of a bio-inspired method (courseworks) (PDP).
SCQF Level: 10.
Credits: 15.