University of York - Computer Science - MSc in Natural Computation

MSc in Natural Computation

Course Structure and Assessment

The subjects taught will cover the following strands:

Bio-inpired computation:
Algorithms for computation that have been inspired by observation of biological systems; simulation of complex biological systems.

Embodied computation:
Physical and bio-chemical systems that are examined from a computational perspective.

Complexity and Emergence:
understanding the properties of natural systems, and how these properties are related to the underlying structures.

The order in which topics may be covered in indicated below - this may well change from year to year. Taken together these topics cover a broad range of natural systems, examining each from a computational perspective. Students must choose a limited number from the topics available, and may bias their learning towards physical systems or biological systems, but will take a core of material in each area.

TERM	TOPIC
Autumn	Neural algorithms - algorithms inspired by natural neural systems.
	Evolutionary algorithms - algorithms inspired by natural evolutionary systems.
	Artificial immune systems - artificial immune systems; social systems; growth.
	Dynamical Systems - differential equations, two dimensional or planar systems, three dimensional systems and the Lorenz attractor.
	DNA and chemical computing - theory and practical knowledge in computational systems inspired by biological and chemical systems.
	Quantum Information Processing - theory of quantum information and quantum computation.
Spring	Emergent systems - complex systems that exhibit emergent properties that cannot be reduced to behaviour of their individual components.
	Simulating biosystems - complex systems perspective of biosystems and the importance of the powerful central concepts of self-organisation and emergence.
	Evolvable hardware - design of systems that can self-adapt as necessary to compensate for changing operational environments or to recover from faults.
	Advanced Dynamical Systems – chaotic systems.
	Adaptive agents - principles of machine learning, focus on two approaches: evolution and inductive logic programming. Agents as a useful abstraction providing a general modelling framework, driven by machine learning
	Advanced Quantum Computation - quantum error correction and fault tolerant schemes for quantum computation.
Summer	Research project and dissertation

Personal Tutor & Tutorial Group

Each student is assigned to a tutorial group (usually containing no more than four or five students), and hence to a personal tutor. Tutorial groups meet on a weekly basis up until the start of the project. The purpose of these meetings is to reinforce the material taught in the formal course units, and also to provide an opportunity for informal discussion of related subjects.

Assessment

Assessment of students' performance in the course modules will take place in a variety of forms: practical exercises, reports, closed examinations, open assessments and a dissertation for the project. Students are deliberately exposed to a variety of assessment methods so that they are not disadvantaged by background. Assessments will take place at various times during the year. Practical exercises, reports and other forms of open assessment will be due either during the course module or just after its completion.

Project Work

The dissertation project undertaken by students over the summer is carried out individually, which might involve collaboration with another organisation. A collaborative project is supervised by a member of the Department, but the collaborating organisation will normally provide an external supervisor.

The subject matter of projects varies widely; most projects are suggested by members of staff, some by external organisations, and some by students themselves, perhaps relating to an area of personal interest that they wish to develop further. All project proposals are rigorously vetted and must meet a number of requirements before these are made available to the students. The department uses an automated project allocation system for assigning projects to students that takes into account supervisor and student preferences.

Contact information: Keith Maynard +44 1904 432712 : keith.maynard@cs.york.ac.uk

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