School of Engineering and Informatics (for staff and students)

Intelligent Systems

(MSc) Intelligent Systems

Entry for 2014

FHEQ level

This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.

General Entry Requirements

If you are a non-EU student and your qualifications (including English language) do not yet meet our entry requirements for admission directly to this degree, we offer a Pre-Masters entry route. For more information, refer to Pre-Masters for international students.

Course Aims

The aim of the MSc programme is to provide a one-year specialist course for very able graduates in arts or science fields, preparing them for research and development work on intelligent systems. A proportion of the students should be able to continue to do DPhil work on fundamental artificial intelligence research or applied research on intelligent systems.

Students are introduced to theoretical issues in artificial intelligence and computing science, and to practical techniques for designing and implementing intelligent systems using a variety of high-level languages.

The course is organised around a small core of compulsory courses leaving a wide choice of optional courses, including courses from Evolutionary and Adaptive Systems, Human-Centred Computing Systems and Cognitive Science, in addition to the traditional areas of Artificial Intelligence such as Computer Vision and Natural Language Processing.

Students will benefit from working in an interdisciplinary research environment and from being taught by specialists from a range of disciplines in the Department of Informatics and Departments Psychology, Philosophy and Biology.

In the final project students build a substantial intelligent system under the supervision of experienced faculty. The course culminates in a degree show where all of the student projects are publicly exhibited.

Having completed the course, students will have the knowledge, skills and competences to take full advantage of innovations in this rapidly developing area.

Course learning outcomes

Demonstrate knowledge and understanding of the concepts, principles and theories of Artificial Intelligence(AI)/intelligent systems.

Demonstrate knowledge and understanding of the current scientific approaches to understanding intelligence, in humans, animals and/or machines.

Evaluate and critically analyse information and argument from a range of disciplines related to AI/intelligent systems.

Analyse and solve problems related to their expertise in AI/intelligent systems.

Demonstrate their ability to extend their basic knowledge to encompass new principles and practice.

Demonstrate their computing, technical and theoretical skills by developing a substantial AI system.

Plan, conduct and report on the development of an AI system.

Engage in independent research.

Communicate the results of study and research, both in writing and orally.

Full-time course composition

YearTermStatusModuleCreditsFHEQ level
1Autumn SemesterCoreApplied Natural Language Processing (955G5)157
  CoreIntelligent Systems Techniques (802G5)157
  OptionAdvanced Software Engineering (947G5)157
  Artificial Life (819G5)157
  Intelligence in Animals and Machines (826G5)157
  Mathematics and Computational Methods for Complex Systems (817G5)157
  Object Oriented Programming (823G5)157
  Real-World Cognition (801G1)157
  Web Computing (927G7)157
 Spring SemesterCoreImage Processing (521H3)157
  CoreMachine Learning (934G5)157
  OptionAdaptive Systems (825G5)157
  Advanced Natural Language Engineering (G5114)156
  Computational Neuroscience (820G5)157
  Generative Creativity (G6004)156
  Neural Networks (807G5)157
  Neuroscience of Consciousness (993C8)157
  Technology-Enhanced Learning Environments (808G5)157

Part-time course composition

YearTermStatusModuleCreditsFHEQ level
1Autumn SemesterCoreIntelligent Systems Techniques (802G5)157
  OptionAdvanced Software Engineering (947G5)157
  Object Oriented Programming (823G5)157
 Spring SemesterCoreImage Processing (521H3)157
  CoreMachine Learning (934G5)157
YearTermStatusModuleCreditsFHEQ level
2Autumn SemesterCoreApplied Natural Language Processing (955G5)157
  OptionArtificial Life (819G5)157
  Intelligence in Animals and Machines (826G5)157
  Mathematics and Computational Methods for Complex Systems (817G5)157
  Real-World Cognition (801G1)157
  Web Computing (927G7)157
 Spring SemesterOptionAdaptive Systems (825G5)157
  Advanced Natural Language Engineering (G5114)156
  Computational Neuroscience (820G5)157
  Neural Networks (807G5)157
  Neuroscience of Consciousness (993C8)157
  Technology-Enhanced Learning Environments (808G5)157

Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.

The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.

School of Engineering and Informatics (for staff and students)

School Office:
School of Engineering and Informatics, University of Sussex, Chichester 1 Room 002, Falmer, Brighton, BN1 9QJ
enquiries@enginf.sussex.ac.uk
T 01273 (67) 8195

School Office opening hours: Monday - Friday 09.00 - 17.00
School Office location [PDF 1.74MB]