Module Overview

Simulation modelling plays an increasingly significant role across modern science and engineering, with the development of computational models becoming established practice in industry, consulting, and policy formulation. Computer scientists are often employed as modellers or software engineers to help in the model development & maintenance cycle. Therefore this is a current and future need for computer science graudates to have a grounding in both the philosophy of modelling in science and various modelling techniques.

This module will familiarize students with general knowledge about the role of modelling in science (with a particular emphasis on computational modelling), will discuss the process of model development and best practice in various stages in the model development cycle. A second (and larger) part of the module will provide a broad survey of the central modelling paradigms. Throughout the module we will demonstrate how computer science techniques are used to develop models in the following domains:

• information networks
• design and management of infrastructure
• epidemics
• natural resource management
• computational economics
• collective robotics
• online trading systems
• climate and Earth system processes

Module Overview

The last decade and a half have seen the Web move away from a purely document-centric information system to one in which hypertext techniques are applied to the sort of data found in databases; the term “Semantic Web” is used to refer to this Web of linked data. Semantic Web technologies enable people to create data stores on the Web, build vocabularies, write rules for handling data, and develop systems that can support trusted interactions over the network. This module looks at the development of the Semantic Web, at the technologies underlying it, and at the way in which those technologies are applied.

Module Overview

Open data, pitched as the raw material of the new industrial revolution, holds great promise, but how do you exploit this new resource?

This course is specifically designed to give students a greater understanding on how to innovate with open data. This course introduces the tools, techniques and skills required to rapidly innovate using data and how to pitch these ideas to potential investors. The course balences technical and non technical content throughout allowing development in all skill areas required to make a career in rich applications using open data. 

Module Overview

Despite the current financial climate, the subject of Computational Finance is both intellectually challenging and attractive to students from an employment point of view. It is thought the MSc in Artificial Intelligence could be made more attractive to overseas students by the introduction of this topic in the curriculum. Also, many graduates from our undergraduate programmes join the financial sector, and are likely to find knowledge of the subject giving them a competitive advantage when attempting to do so. Elsewhere in the University there are modules and programmes in Mathematical Finance, and this module will be designed to be distinct from them in emphasizing the computational aspect, in a hands-on teaching environment.

To do this module, you should be competent in basic calculus, including ordinary differential equations, and programming in some high level language (e.g. Java)

Module Overview

Biometrics is about how we can recognise people automatically, by personal characteristic. We all have fingerprints and faces - and they are unique. We have to sense the information, process it and then deliver an assessment of the identity associated with that data. That's what this course is about: it's about electronics, computer science, maths, and pattern recognition. It assumes you have numerate skills, and can program a computer in some way. The course does rely much on computer vision, as most biometrics technologies are based on computer vision. Some grounding in this will be part of the course. You might choose to take this course if you are interested in cutting edge technology, much of which is still in a research stage, which whilst benefitting, even challenges the way society operates. The course will be given by Mark Nixon who has been involved in biometrics from its infancy, and who has pioneered biometrics technologies (gait, ear and soft...... yes "soft"), all at Southampton. The course has evolved from many professional courses, professional tutorials (IEEE/ IAPR etc) and from the many keynote/ plenary lectures that I (Mark) have given over the years. The course will be challenging, but also should be a very interesting and enjoyable introduction to an area of topical interest worldwide.

Module Overview

This module aims to introduce the area of software verification. A range of approaches will be covered.  The use of specification languages, including logics, and the use of tools to support verification will be explored. The module will provide practical experience in using modern verification tools as well as covering some of the theory underpinning the correctness of these tools. The module is optional but MSc Software Engineers must choose this module and/or Automated Code Generation.  The pre-requisite knowledge is basic familiarity with discrete mathematics and first-order logics.  

Module Overview

This module aims to cover the topic of production of code via automated means. This topic incorporates the different approaches to metaprogramming that are available in mainstream languages and also touches on issues such as automated generation of testing code and software product lines.  The course is practical in nature and reasonable programming skills in Java and possibly also C++ are required. The module is optional but at least one of this module and Automated Software Verification must be taken by MSc Software Engineering students. 

Module Overview

• To introduce key concepts in pattern recognition and machine learning; including specific algorithms for classification, regression, clustering and probabilistic modeling.
• To give a broad view of the general issues arising in the application of algorithms to analysing data, common terms used, and common errors made if applied incorrectly.
• To demonstrate a toolbox of techniques that can be immediately applied to real world problems, or used as a basis for future research into the topic.

Module Overview

Aims

• To introduce the students to the key issues of interaction of multiple self-interested parties (a.k.a. agents) and give a broad survey of topics at the interface of theoretical computer science and game theory dealing with such interactions.
• To provide the theoretical background and practical tools to solve problems arising in settings with self-interested participants, to predict possible behaviour and outcomes, and finally, to design multi-agent systems that would incentivise desirable behaviour.
• To introduce the students to the specifics of computational game-theoretic techniques in different application areas, ranging from multi-agent systems, electronic marketplaces and networked computer systems to computational biology and social networks.
• To extend and advance the knowledge obtained in other AI modules (in particular, COMP6XXX Intelligent Agents). 
  

Module Overview

This module aims to:

• Explain the role of database systems in information management.
• Introduce students to standard UNIX tools and techniques (e.g., vi editor, ed, sed and awk).
• Develop the student's skills in the effective use of Unix tools for document preparation, software development and system administration.
• Introduce students to command line working and to help develop their appreciation of its strengths/weaknesses relative to GUIs.