design intelligence

Iain Robinson - Education

2001 - 2005

BSc Computer Science & A.I.

University of Sussex, Brighton, Sussex

My chosen degree programme placed great emphasis on providing students with both theoretical understanding and practical experience of the majority of the topics covered. As a result I was able to develop my existing programming skills and gain experience of new programming languages. I learned to work well in teams and individually, to devise creative solutions to problems. I improved my ability to present ideas and work (both verbally and on paper) and, through ongoing assessment, to work to strict deadlines.

The degree concentrated on the fields of computer science and AI in equal measure. Modules related to the computer science aspect provided me with a good overall understanding of the design, construction and operation of computer hardware, operating systems, software and networks while modules relating to AI complemented these by examining all areas of work carried out in the field of AI/ALife.

The following is an outline of the topics covered by the course:

Year 1
Data Structures
Foundation & Further Artificial Intelligence
Foundations of Computer Science
Foundation & Further Programming
Introduction to Logic
Mathematical Tools for Cognitive Scientists
Software Design
Technical Communication Skills
Year 2
Algorithmics
Computer Systems Architecture
Computer Vision
Functional Programming
Introduction to Operating Systems
Languages & Compilers
Multimedia Communications Technology
Non-Symbolic Artificial Intelligence
Software Engineering
Symbolic Artificial Intelligence
Year 3
Adaptive Systems
Animal & Machine Intelligence
Data Mining
Distributed Systems
Internet Technologies
Space Systems
2000 - 2001

Artificial Intelligence for Technology

The Open University, Milton Keynes, Buckinghamshire

The course was divided into two main topics:

  • Knowledge Based Systems - introduced the concepts and applications of rules and the handling of uncertainty in KBSs with particular reference to Bayesian updating, certainty theory and fuzzy logic.
  • Artificial Neural Networks - focused on the practical applications and underlying mechanisms of single and multi-layer perceptrons, Hopfield networks and Kohonen networks.