Overview
Broaden your expertise through the use of Artificial Intelligence (AI) techniques and big data. 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 workplace. It's been designed in response to the high demand for experts in big data analysis and modelling. Choose to take an optional 12-week work placement as part of your course and boost your employability.
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
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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.
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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.
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Develop your skills in preparation to undertake independent research at the MSc level. In particular, you will develop skills to get you started and lay the essential foundations for your Masters Research Project. During the module you will hone your specific research interests whilst recognising time constraints and other practical considerations. These usually set, and limit, the topic, the research approach and study, and the selection of suitable methods. The module will cover: the scientific process, logical thinking and experimental design; critical evaluation of the literature; data analysis using the freely available statistical software R from different perspectives to tackle a range of experiments; and writing a research proposal for an MSc project. You will learn through face-to-face tutorial-style sessions, supplemented by structured reading and exercises. During the module you will be supported in defining your area of study and planning your research project. As part of this process you will be expected to undertake a critical review of the appropriate literature and other information relevant to the proposed project. You will need to give consideration to the logistics required, provide experimental design criteria (where appropriate) at least for the initial stages of the proposed work, and also take account of all health and safety regulations (including COSHH) and appropriate ethical considerations. Designing research projects and writing research proposals and taking responsibility are crucial skills for careers at postgraduate level, where you may have to consider deadlines (time management), ethical issues and assessing risks. Experience of using freely available analytical software also gives you an advantage.
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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.
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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.
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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.
Optional modules (subject to availability)
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Put your skills into practice during your placement module, with real life issues in a work environment. You will get the opportunity to gain work experience in different environments and tackle issues using problem evaluation, solution and analysis gaining a deeper understanding of the industry/business sector. Learning key soft skills in a professional environment will help to enhance your employability after completing this course. You will have access to support to help you locate a placement but will also be expected to be in good academic standing before being permitted to start the placement. Once in a placement you will be allocated a link tutor within the course team who will visit you and help to ensure the placement runs as expected. At the end of your placement you will be expected to be able to critically reflect on your experience and demonstrate how you have applied your theory and learning to date within a work-based environment by writing a reflective report on the placement, underpinned by a log of work done. This log will help you to demonstrate your professionalism, leadership skills, and knowledge to prospective employers and also gain key communication and personal skills. The University placement team will provide guidance and support to assist you in obtaining a placement. However, securing a placement will ultimately be your responsibility. To ensure you're prepared for work in industry, you are required to pass all taught modules before undertaking a placement. You will need to have secured an acceptable placement by the specified deadline. Upon securing an acceptable placement you will be required to pay the placement fee of £625. If you have not secured an agreed placement by the deadline, you will automatically be transferred to the one-year Masters course (without placement).
Assessment
Throughout the course, we’ll use a range of assessment methods to measure your progress. You’ll complete exams, essays, research reports, oral presentations, and a dissertation on a subject of your choice.
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
Our campus is close to the centre of Cambridge, often described as the perfect student city.
Fees & funding
Course fees
UK students starting 2023/24 (full-time, per year)
£10,000
International students starting 2023/24 (full-time, per year)
£15,800
Placement (UK and international students)
£625
How do I pay my fees?
UK students
You can pay your fees upfront, in full or in instalments – though you won't need to pay until you've accepted an offer to study with us. Find out more about paying your fees.
International students
You can pay your fees upfront, in full or in two instalments. We will also ask you for a deposit of £4,000 or a sponsorship letter. Details will be in your offer letter.
Funding for postgraduate 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 and bursaries, which provide extra financial support while you're at university. These include an Alumni Scholarship, worth 20% off fees for ARU graduates.
International students
As well as a number of scholarships, we offer an early payment discount. Explore your options:
Entry requirements
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@aru.ac.uk for further information. Applicants to undergraduate courses can also check our information about undergraduate entry qualifications.
You'll need a computer and reliable internet access to successfully engage with your course. Before starting a course, we recommend that you check our technical requirements for online learning.
International students
We welcome applications from international and EU students, and accept a range of international qualifications.
You'll need a computer and reliable internet access to successfully engage with your course. Before starting a course, we recommend that you check our technical requirements for online learning.
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.
Check the section 'Standard entry requirements' for IELTS requirements for this course.
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.
