Artificial Intelligence and Big Data MSc

Postgraduate (1 year full-time)

Cambridge

September

Overview

Broaden your expertise through the use of Artificial Intelligence (AI) techniques. In a world reliant on data to improve processes and monitor success, this course will help you gain transferable skills in AI to benefit your work place. Our course was designed in response to the high demand for experts in big data analysis and modelling.

Full description

Careers

Cambridgeshire is home to companies that are looking for graduates with skills in machine learning and artificial intelligence techniques that our course will provide. We have chosen the modules for this course to ensure the curriculum reflects the modern trends in Artificial Intelligence. Each module gives you a glimpse into the workings of the IT industry and presents you with topics that prepare you for entering the job market.

Following this course you may wish to apply your skills in a variety of job roles where there is a need for AI-enabled business strategies and marketing, management of database records, e-business technologies, IT system development and design or IT project management.

If you'd like to continue your studies in research or further education, Anglia Ruskin offers a wide range of full-time and part-time postgraduate research degrees including MPhil or/and PhD in Computer Science, or even a DProf in Science and Technology.

Modules & assessment

Core modules

  • Semantic Data Technologies
    Businesses, large organisations and government departments at a local and European level are increasingly producing and using large semi structured data generated from data collection from their own activities and from the wider internet and social media. Semantic Data Technologies both identify and interpret the meaning of data according to its context. This module introduces this concept, alongside the key technologies and techniques for storing data and develops the skills needed for sophisticated data management. The technologies supporting the 'semantic web' have provided the tools, methodologies and theoretical underpinnings to enable data to be automatically interpreted by machines for knowledge based tasks. These techniques are increasingly being used in a more general approach to handling the kind of non-structured data that is important for recording, evaluating and guiding policy and decision making processes. This module will provide the knowledge and skills for students to structure semantic data, develop ontological models and use these to create knowledge based applications to analyse data, support decision making, enable intelligent access to information and add value to data. After completing this course students will be able to design and implement applications that comply with data re-use standards, utilise the semantic web as well as applying those technologies to the organisation and analysis of big data. The knowledge and skills learned in this module complement those of information system analysis design and data base implementation as well as advanced web server and application development, providing a theoretical and practical base for enterprise wide data handling.
  • Advanced Machine Learning
    Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module you will learn about the tools and algorithms that can be used to create machine learning models. Big data and their economic, legal and ethical aspects are explored, along with data acquisition and pre-processing methods that are used to make these suitable for machine learning algorithms. You will also look into how large data sets should be divided into a training set and a test set and different types of problems that can be solved with machine learning will also be introduced. A range of parametric algorithms such as linear regression, logistic regression, and non-parametric algorithms such as K-Nearest neighbour, decision trees, SVMs, will be discussed. To be able to evaluate a model, a few performance metrics will be explored, the metrics chosen influence how the performance of machine learning algorithms are measured and compared. An important concept that you have to be aware of when training machine learning algorithms is ‘overfitting’, an over fitted model will have a low accuracy and therefore you will learn how to use regularization to avoid overfitting.
  • Research Methods
    Gain support and foundations in the research skills needed for your Masters level dissertation. You’ll investigate research activities including project management, research project design and analyses, ethical considerations and dissertation preparation.
  • Applications of Machine Learning
    This module builds on, and extends the Advanced Machine Learning module by looking at two main applications of machine learning – image recognition and natural language processing. You will study various algorithms for image recognition and will do a variety of experiments, including hand writing recognition, face recognition, medical picture analysis and speed detection. You will explore different machine learning models that can be used in number of natural language processing tasks such as tokenization, named entity recognition, and classification. You will also investigate and experiment with the models and algorithms learned during practical sessions.
  • Neural Computing and Deep Learning
    Deep learning and neural networks have revolutionised numerous fields in recent years. From smartphones and smart watches to cars and even house appliances, electronic devices are increasingly making use of machine learning and neural computing to take decisions, categorise and classify items, learn behaviours, assist us with choices and make prediction. The near future will see an even larger number of “self-learning” devices in almost every aspect of our lives. This module explores two main areas of Intelligent Systems: neural networks and deep learning. You will start analysing the structure of neural networks, from the theoretical aspects to the practical implementations, both biological and artificial. You will then move to the concept of supervised and unsupervised learning and analyse some of the most widely used deep learning methodologies. You will cover some of the main models and algorithms for regression, classification, clustering and decision making processes. The module will include applications of neural computing and deep learning to big data in physical and biological sciences, finance and social sciences. You will use primarily the Python programming language and requires familiarity with basic linear algebra, probability theory, and programming in Python.
  • Major Project
    This module supports students in the preparation and submission of a Master's stage project, dissertation or artefact. The Module provides the opportunity for students to select and explore in-depth, a topic that is of interest and relevant to their course in which they can develop a significant level of expertise. It enables students to: demonstrate their ability to generate significant and meaningful questions in relation to their specialism; undertake independent research using appropriate, recognised methods based on current theoretical research knowledge, critically understand method and its relationship to knowledge; develop a critical understanding of current knowledge in relation to the chosen subject and to critically analyse and evaluate information and data, which may be complex or contradictory, and draw meaningful and justifiable conclusions; develop the capability to expand or redefine existing knowledge, to develop new approaches to changing situations and/or develop new approaches to changing situations and contribute to the development of best practice; demonstrate an awareness of and to develop solutions to ethical dilemmas likely to arise in their research or professional practice; communicate these processes in a clear and elegant fashion; evaluate their work from the perspective of an autonomous reflective learner.

Assessment

Where you'll study

Your faculty

The Faculty of Science & Engineering is one of the largest of the four faculties at Anglia Ruskin University. Whether you choose to study with us full-time or part-time, on campus or at a distance, there’s an option whatever your level – from a foundation degree, BSc, MSc, PhD or professional doctorate.

Whichever course you pick, you’ll gain the theory and practical skills needed to progress with confidence. Join us and you could find yourself learning in the very latest laboratories or on field trips or work placements with well-known and respected companies. You may even have the opportunity to study abroad.

Everything we do in the faculty has a singular purpose: to provide a world-class environment to create, share and advance knowledge in science, technology and engineering fields. This is key to all of our futures.

Where can I study?

Cambridge
Lord Ashcroft Building on our Cambridge campus

Our campus is close to the centre of Cambridge, often described as the perfect student city.

Explore our Cambridge campus

Fees & funding

Course fees

UK & EU students, 2019/20 (per year)

£8,500

International students starting 2019/20 (per year)

£13,700

How do I pay my fees?

Paying upfront

You won't need to pay fees until you've accepted an offer to attend, but you must pay your fees up-front, in full or in instalments.

How to pay your fees directly

International students

You must pay your fees up-front, in full or in instalments. You will also be asked for a deposit or sponsorship letter/financial guarantee. Details will be in your offer letter.

Paying your fees

Funding for UK & EU students

It’s important to decide how to fund your course before applying. Use our finance guide for postgraduate students to learn more about postgraduate loans and other funding options.

We offer a fantastic range of ARU scholarships, which provide extra financial support while you're at university. Find out more about eligibility and how to apply.

Funding for international students

We offer a number of scholarships, as well as an early payment discount. Explore your options:

Entry requirements

Loading... Entry requirements are not currently available, please try again later.

Important additional notes

Our published entry requirements are a guide only and our decision will be based on your overall suitability for the course as well as whether you meet the minimum entry requirements. Other equivalent qualifications may be accepted for entry to this course, please email answers@anglia.ac.uk for further information.

International students

We welcome applications from international and EU students, and accept a range of international qualifications.

English language requirements

If English is not your first language, you'll need to make sure you meet our English language requirements for undergraduate courses.

Improving your English language skills

If you don't meet our English language requirements, we offer a range of courses which could help you achieve the level required for entry onto a degree course.

We also provide our own English Language Proficiency Test (ELPT) in the UK and overseas. To find out if we are planning to hold an ELPT in your country, contact our country managers.

Suggested courses that may interest you

Artificial Intelligence with Cyber Security

Full-time postgraduate (1 year)

Cambridge

September

Intelligent Systems and Machine Learning

Full-time postgraduate (1 year)

Cambridge

September

Computer Science

Full-time postgraduate (12 months, 15 months)

Cambridge

January, September

Get more information

UK & EU applicants

01245 68 68 68

Enquire online

International applicants

+44 1245 68 68 68

Enquire online