Computing

Adaptive Systems

Module code: 825G5
Level 7 (Masters)
15 credits in spring teaching
Teaching method: Laboratory, Lecture, Seminar
Assessment modes: Coursework

You gain an understanding of various adaptive processes occurring in both the animal and physical worlds and are equipped with the theoretical and practical tools to study and develop such adaptive mechanisms.

You study key concepts including:

  • cybernetics
  • control theory
  • self-organisation
  • autonomous robotics
  • evolutionary and developmental robotics
  • dynamical systems approaches to embodied cognition.

It provides you with an opportunity to gain implementation-level familiarity with a variety of adaptive algorithms and techniques and how to apply them in problem solving and biological modelling.

You will gain sufficient experience of using such techniques in a programming project containing structural elements of a research project (hypotheses, extension of previous work, or novel applications).

Pre-requisite

The module assumes an ability to write software in one appropriate programming language (e.g. Java, C, Python, Matlab). Basic knowledge of formal computational skills is also assumed.

Module learning outcomes

  • Recognise, describe and model adaptive processes in natural and/or artificial systems.
  • Critically evaluate approaches to developing adaptive behaviour.
  • Demonstrate implementation-level familiarity with a variety of adaptive algorithms and techniques and apply them in problem solving biological modelling.
  • Deploy such techniques in a research project.