入学要求：
学术要求： Typically applicants will have achieved at least an upper second class honours degree (or international equivalent) in Computer Science or a related discipline with an appropriate mathematical basis. We will consider applicants who do not have an appropriate Computer Science qualification but have compensatory experience, for example appropriate industrial experience.
Applicants are required to nominate two referees, of which at least one should be from the applicant's current employer or place of study. Applicants are normally interviewed before acceptance either in person if UK based or by telephone for international students.
英语要求： IELTS 6.5 (min 6.0 each)
学费Tuition Fee：2011/2012 International Students: £15,600
课程特征 Course Features：
MSc in Natural Computation covers the topics of Bio-inspired, quantum and complex systems. The course aims to provide participants with a thorough grounding in the use of advanced techniques of natural computation - broadening ideas about computation to include ideas from mathematics, physics, electronics and biology. It is aimed at graduates with a first degree in Computer Science or Computer Science/Mathematics joint honours who wish to develop knowledge and skills in this area before undertaking industrial work or academic study. Appropriate recent experience may also qualify you for the course, if you do not have an appropriate Computer Science degree.
The unique emphasis of the MSc Natural Computation course is on developing the computational view of natural processes, rather than considering particular aspects of nature-inspired computation, or concentration on the application of techniques in a particular domain.
课程内容 Course Content：
The subjects taught will cover the following strands:
Bio-inspired 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.
Taken together these topics cover a broad range of natural systems, examining each from a computational perspective. In addition to the mandatory Neural Computing, Evolutionary Computation and Swarm Intelligence students must choose 60 credits from the remaining modules options.
| The components of the course for 2011/2012 are listed below: |
| Module Title |
Term |
Short Description |
| Neural Computing |
Autumn |
Algorithms inspired by natural neural systems (Mandatory). |
| Evolutionary Computation |
Autumn |
Algorithms inspired by natural evolutionary systems (Mandatory). |
| Swarm Intelligence |
Autumn |
An introduction to the basic biology, algorithms, and uses of a range of swarm intelligence approaches (Mandatory). |
| Quantum Computation |
Autumn |
An introduction to the theory of quantum computation. In it we will learn about the pioneering quantum algorithms that promise a qualitative leap in computation power over conventional computers (Optional). |
|
User Centred Design
|
Autumn
|
User Centred Design introduce students the field of Human-Computer Interaction (HCI). This field covers all aspects of people's interactions with digital systems (Optional).
|
|
Emergence
|
Spring
|
Complex systems that exhibit emergent properties that cannot be reduced to behaviour of their individual components (Optional).
|
|
Complex Dynamical Biosystems
|
Spring
|
Complex systems perspective of biosystems and the importance of the powerful central concepts of self-organisation and emergence (Optional).
|
|
Computing with Biology and Chemistry
|
Spring
|
Theory and practical knowledge in computational systems inspired by biological and chemical systems (Optional).
|
|
Quantitative Research Methods
|
Spring
|
An introduction to experimental design and statistics as used in HCI and computer science for the evaluation of interactive systems (Optional).
|
|
Quantum Information Processing
|
Spring
|
An introduction to the theory of quantum information and quantum communication (Optional).
|
|
Adaptive & Learning Agents
|
Spring
|
This module is situated at the intersection between Machine Learning and Agents. The module covers background in both areas followed by a discussion on the methodology of the emerging AI domain of Adaptive and Learning Agents, and a demonstration of its ideas on a few focus topics (Optional).
|
|
Final Project - Natural Computation
|
Summer
|
A substantial, independent research project building on the taught course. The deliverable is a dissertation.
|
教学与评估 Teaching and 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.
Timescales, Modules and Project Descriptions may be subject to change.