F21BC - Biologically Inspired Computation
Course leader(s):
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.
Syllabus
1. Neurocomputing and deep learning (1.1 1. Gradient descent, 1.2 2. Neural networks, 1.3 3. Deep learning, 1.4 4. Neuroevolution)
2. Evolutionary computation (2.1 1. Optimisation, 2.2 2. Evolutionary algorithms, 2.3 3. Genetic programming, 2.4 4. Multiobjective evolutionary algorithms)
3. Swarm computing (3.1 1. Swarm intelligence, 3.2 2. Ant colony optimisation, 3.3 3. Particle swarm optimisation)
4. Cellular automata (4.1 1. Computational universality, 4.2 2. Game of life, 4.3 3. Elementary cellular automata)
Learning outcomes
By the end of the course, students should be able to do the following:
- explain limitations of traditional computation models
- analyse a range of biologically inspired computation models and algorithms, explain their limitations, and illustrate areas of applicability
- implement one or more biologically inspired models and algorithms to solve a practical problem
- identify bio-inspired models and algorithms that apply to optimisation and machine learning problems
- exercise substantial autonomy and initiative when solving a real-world problem using bio-inspired techniques
- demonstrate critical reflection informed by research when analysing experimental results and comparing bio-inspired models and algorithms
Further details
Curriculum explorer: Click here
SCQF Level: 11
Credits: 15
