About this course
About
Modules
The MSc award consists of six compulsory taught modules (totaling 120 credits) and a substantial piece of independent Masters Applied Research Project (120 credits).
Machine Learning
Machine learning is the branch of artificial intelligence concerned with algorithms and statistical models that use data for various tasks. This module will provide students with the mathematical and statistical knowledge to understand the foundations of common supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. State-of-the art methods including probabilistic programming and explainable AI will also be introduced.
Big Data & Infrastructure
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources. The core concepts of distributed computing will be examined in the context of Hadoop. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
Deep Learning and Its Application
Deep learning is an emerging and important focus area of Artificial Intelligence (AI). It aims to learn models and patterns as in conventional machine learning approaches, but it has the ability to discover more accurate representations without manual intervention for new types of domains. More recent advances on deep learning have led to very successful applications in computer vision, natural language processing, autonomous computing, etc. The module introduces the fundamental concepts of deep learning, neural networks as well as the theory associated with the development of successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimization process along with development tools, and apply them to the development of solutions for deep learning application domains (i.e. Computer Vision, Natural Language Processing, etc.)
Statistical Modelling & Data Mining
This module first provides a systematic understanding of probability and statistics. It then provides an in-depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as Python, R and Weka. Online tools, such as Blackboard will be used to facilitate blended learning approach. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real-world contexts.
Autonomic Computing & Robotics
Autonomic computing is a self-managing computing model (self*) named after, and inspired by, the human body's autonomic nervous system. The goal of autonomic computing is to enable systems to run themselves, providing the capability of high-level functioning (autonomy) while keeping the system's complexity invisible to the user. An autonomous robot is a robot that performs behaviours or tasks with a high degree of autonomy. Autonomous robotics is usually considered to be a sub-field of artificial intelligence, robotics, and information engineering. Our proposition is that to enable a truly autonomous robot, it requires autonomicity. This module will introduce students to the self-managing systems area of Autonomic Computing and its cross-over into AI & (Autonomic) Robotics. These self* principles will be able to be applied to the advanced automation of any system not just AI & Robotics.
Knowledge Engineering & Computational Creativity
Knowledge Engineering is a branch of artificial intelligence concerned with the process to build, maintain, use and extend knowledge-based systems that use (symbolic) AI methods. Computational creativity can be defined as the use of an algorithmic approach/perspective to gain a better understanding of human creativity or AI to replicate human-level creativity using computing. This module introduces students to the fundamentals of knowledge engineering and computational creativity, including terminology and concepts, core models and algorithms, technologies, and application scenarios. Students will be introduced to data science toolkit that can be applied to build knowledge-based applications. Material covered will be contextualised by providing examples of the latest research within the area. Through completion of the module, students will demonstrate qualities including subject-specific knowledge and skills and the ability to use flexible and creative approaches to problem solving and communication.
Masters Applied Research Project
Masters Applied Research Project provides the opportunity for you to demonstrate independence and originality, to plan and organise a large project and to implement this over a sustained period of time. The project will follow a user centred design approach and requires you to identify, define, develop, evaluate and implement a novel solution in a selected application area. This will require you to put into practice the techniques you have been taught throughout the course. With this in mind, the project must be tightly constrained to be achievable, whilst sufficiently open enough for you to demonstrate your ability to engage in innovative approaches towards a contribution to research knowledge.
The applied research project offers an opportunity to deepen your knowledge and develop your skills regarding an area in which you have a special interest. Focusing on this specialised area, the project should investigate all aspects of the user centred design and development process from project definition and ideation through to development, evaluation and implementation. In doing so, a full appreciation for the research / innovation process and how this could be transferred into business should be developed. This will allow you to investigate the pathways to impact from an academic, commercial and social aspect.
The project will undertake an applied piece of research which is novel yet realistic and which builds upon skills and knowledge developed throughout the course. The project will be motivated through a critical evaluation of existing literature and theory and the identification and justification of novel research questions. The project tests the inventiveness, the critical capacities, the project management and the in-depth knowledge and problem-solving skills of the student.
The project must involve design, implementation, experimentation and critical analysis of results, benchmarked against other approaches drawn from the literature. This should follow the user centred design process.
Attendance
Typically 15 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Masters Applied Research Project takes place in the final two semesters of the course seperately.
Start dates
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September 2021
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January 2022
Teaching, Learning and Assessment
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
The course is assessed by 100% coursework.
Content
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
Attendance and Independent Study
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules (more usually 20) and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
Calculation of the Final Award
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Academic profile
We have a highly experienced and energetic course team, in terms of both teaching and research. All members of the course team are research active and will be included in the forthcoming REF2021 submission. The course team have been grantholders of multi-million pound research projects, they have produced world-leading and internationally excellent research outputs in the area and have demonstrated research impact from their endeavours. We also fully embrace the importance of innovative teaching and assessment methods and are all Fellows of the Higher Education Academy in the UK.
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (18%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2019-2020.
