MSc Specialist Areas

Our MSc degrees includes two semesters of lecture-based teaching, between September and April, after which you will concentrate, full-time, on a major individual project, leading to a dissertation in your chosen area of specialisation. Your taught courses are selected from a range of around 50 course options, a reflection of the breadth of research in Edinburgh. The courses are grouped into the following specialist areas:

Analytical and Scientific Databases

This specialism brings together topics in advanced database design and optimisation theory and implementation that will be applicable to the applied as well as the research fields. There are two central academic outcomes to this programme. The first is to bring the students up to speed with the latest technology in Database Science and in the analysis of complex databases. The second aim is to introduce the students to the active research areas in the field within the context of a range of real example programs.

Bioinformatics, Systems and Synthetic Biology

The aim of the bioinformatics specialism is to familiarise students with biological data, its storage and analysis. In particular, students should understand what information can be extracted from biological data (e.g., information related to phylogenetic trees, metabolic networks, protein structure and function, developmental processes, genetic correlates of disease, etc.) and what techniques can be used for extracting such information. Students who complete the course will be prepared for employment in the bioinformatic sector of pharmaceutical and biotech industries or for entry into a PhD programme in Bioinformatics.

Cognitive Science

The Cognitive Science specialist area gives students an opportunity to study the structure and behaviour of both natural and artificial cognitive systems. Relevant cognitive processes include language, reasoning, vision, and learning, which can be studied from neural, probabilistic, and symbolic viewpoints. Students are encouraged to also take courses from the School of Philosophy, Psychology and Language Sciences (PPLS).

Computer Systems, Software Engineering and High Performance Computing

This specialism embraces both the theory and the practice of designing programmable systems, with topics ranging from advanced programming concepts to the design of computer systems and software engineering. As with other specialisms, this specialism prepares students for PhD study and for careers in the software industry.

Design Informatics

The School of Informatics along with Edinburgh College of Art have launched a series of new masters courses for 2013 which will specialise in Design Informatics. Design Informatics brings together elements from both schools; product design, media, fashion, architecture, vision and robotics, speech and language technology, sensor networks and machine learning. Design Informatics aims to equip students with the ability to design a product and take that product to market.

Intelligent Robotics

The aim of the Intelligent Robotics specialism is to prepare students for entry into PhD programmes or for employment as research workers in Intelligent Robotics and related areas in higher education or industrial/commercial research laboratories undertaking research and development in robotics and intelligent control applications.

Agents, Knowledge and Data

The "Agents, Knowledge and Data" specialist area spans a range of topics from foundations of Artificial Intelligence (such as computational logic, knowledge representation and reasoning, decision and game theory) to practical real-world "collective intelligence" technologies (linked and open data, human-computer collaboration, big data applications), exposing students to a broad range of computational methods for dealing with next-generation large-scale intelligent systems.

Machine Learning

Increasing amounts of data are being captured, stored and made available electronically. The aim of the Machine Learning specialism is to train students in techniques to analyze, interpret and exploit such data, and to understand when particular methods are suitable and/or applicable. These techniques derive from disciplines such as probabilistic and statistical modelling, pattern recognition and neural networks, and are sometimes collectively referred to as data mining. The specialism will prepare students for entry into PhD programmes or for employment in commercial environments and/or scientific/engineering research.

Natural Language Processing

The aim of the Natural Language Processing specialist area is to prepare students for entry into PhD programmes or for employment in industrial laboratories undertaking research and development in natural language and speech processing. In this specialist area, the programming requirement should be fulfilled by taking Computer Programming for Speech and Language Processing. Students are encouraged to also take courses in speech processing or psycholinguistics in the School of Philosophy, Psychology and Language Sciences (PPLS).

Neural Computation and NeuroInformatics

This specialism prepares students for entry into Ph.D. programmes or for employment as research workers at the intersection of the study of the brain, cognitive science, and the study of its computation. It ranges from the study of cellular and subcellular computational processes, to software methodologies for brain research - the emerging field of neuroinformatics. In particular, students will be well prepared by this specialism to apply for entry to the Doctoral Training Centre in Neuroinformatics in Edinburgh.

Theoretical Computer Science

This specialism will be of particular interest to students with a strong mathematical background. It introduces students to core areas of Theoretical Computer Science, and provides a link from theoretical topics to their application in software and hardware systems development. In many courses the theory sgests the construction of tools to aid software and hardware design and verification. The practical components of these courses introduce the technologies through which theory-based tools are implemented and provide experience of the practical application of theory across a range of theory-based Systems Engineering tools. Students meet a variety of these tools during the course and develop skills in their use, as well as studying the underlying theory and techniques used in their implementation.

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