Online Applied Artificial Intelligence MSc

Postgraduate degree

Master the analysis of complex data and hone the skills to develop AI applications in support of business decision-making with our flexible online MSc in applied AI.

  • Develop the skills and knowledge to open doors in your career
  • Supported online learning gives you the flexibility to fit study around your other commitments
  • Learn from experienced and passionate professionals
  • Ideal if you work with AI or have no previous experience
  • Be part of the University of the Year (THE)


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About your masters in AI

Our flexible online Applied Artificial Intelligence Masters has been designed to give you the expert knowledge and skills to analyse complex data and develop Artificial Intelligence applications. This course is ideal if you work in business or technology and want to upskill in your existing role to support your career aspirations. It’s also ideal for anyone with a passion for AI and is perhaps looking to switch careers, or retrain, but has no prior exposure or relevant degree in computing or AI. Our master’s in applied AI has been designed to be completely asynchronous giving you the flexibility to study, learn and grow, around your other commitments.

Course highlights

  • Advance your career in applied artificial intelligence
  • Develop the skills to analyse complex data and develop Artificial Intelligence applications
  • Designed in response to the high demand for experts in AI, machine learning and data mining
  • Ideal if you have no previous experience or degree in computing or AI
  • Receive support every step of the way from our dedicated distance learning team

How you'll study

This course is studied 100% online
Course length

Study this course over 3 years

Start dates

Start in September

Application deadline

Our next deadline is

28th June 2024

UK Quality Assured

Course overview

Artificial Intelligence (AI) promises to provide the tools to enhance technology, business models, and decision-making across a range of sectors, from industrial automation, manufacturing, transport, banking, and cybersecurity to health, human resource management, and social care.

If you are planning to align your career towards the ever-growing and popular field of artificial intelligence, and your undergraduate degree did not prepare you for it, this MSc Applied Artificial Intelligence course provides you with an excellent opportunity. This course is also suitable for anyone working in a related field who wants to develop their skills, knowledge and confidence to advance their future career prospects.

This course takes you, step by step, through the major building blocks of AI in an applied fashion and will allow you to gain highly marketable skills including programming, data acquisition and cleaning. Our MSc in AI also offers practical data analytics, feature extraction, and machine learning skills, tailored to comply with the current market demands, technologies, and industrial practices.

The course is designed in response to the high demand of experts in AI, machine learning, data mining, big data analysis and modelling and will support career opportunities in a range of fields that make use of artificial intelligence including life sciences, human resources, business administration, marketing, psychology, finance and banking.

As well as completing the core modules you’ll need to complete 30 credits of optional modules which allows you to focus on areas of particular interest.

The major project will provide you with a platform to showcase your skills and knowledge on a topic relevant to your specialism, experience, or career aspirations.

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Modules

Core modules

Analytical Techniques

This module is intended to provide a general introduction to the analytical techniques needed for artificial intelligence and is tailored for you to develop core maths knowledge that artificial intelligence is built upon. The module will enable you to assess your existing mathematical skills and sympathetically enable you to gain knowledge and skills and/or remedy any basic deficiencies.

The learning topics mainly cover the following areas: basic functions, variables, equations and graphs; linear algebra; probability and statistics. These topics will enable you to have a solid grasp over the most essential maths concepts for machine learning and to explore applying the core mathematical models to problems of regression and classification in a real-life work environment.

Programming with Python

This module introduces high-level computer programming using Python, one of the most powerful yet intuitive programming languages. It is designed to benefit students with no prior programming experience. This module enables you to understand the underlying concepts, principal components, design elements of computer programming and then it gradually leans towards technical aspects of programming by putting theoretical programming concepts into practice. This module employs real-world scenarios and case studies from various industrial domains and practices such as healthcare, marketing, finance, business, etc. to introduce programming concepts and skills. Best programming practice will be taught and used to ensure a maximum level of productivity and quality. In addition, methods and techniques will be introduced and applied to the validation and verification of software quality and standards.

Machine Learning Techniques

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’ll learn about the tools and algorithms that can be used to create machine learning models. We’ll also investigate how large data sets should be divided into a training set and a test set. Different types of problems that can be solved with machine learning will be introduced; supervised learning algorithms and examples of where they can be applied will be presented. 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 have influence on how the performance of machine learning algorithms is measured and compared. An important concept that we must be aware of when training machine learning algorithms is 'overfitting'. You will learn to apply regularisation techniques to mitigate overfitting and enhance model performance.

You’ll investigate and experiment with the various models and algorithms covered in the module using standard libraries such as scikit-learn, statsmodels and Python ML packages, gaining practical skills that are directly applicable in real-world scenarios.

Postgraduate Major Project

This module supports students in the preparation and submission of a Masters Stage Dissertation or Project worth 60 credits.

The topic will be based around a project relevant to your specialism and experience. It will be completed solely by yourself as an individual project and, if part of a larger project, the aspect implemented must be clearly identifiable as your sole work. Group projects are not allowed.

The project outline will be formulated and agreed after discussion between yourself and a supervisor at ARU and must meet the objectives of an ARU postgraduate major project.

During the implementation, you will be expected to identify / formulate the problem and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop hardware/software and/or media artefacts as appropriate, process data, critically appraise and present your findings using a variety of media.

Regular meetings with the project supervisor and workplace mentor should take place, so that the project is closely monitored.

Optional modules

In addition to the core modules above you will need to complete 30 credits from the optional modules below.

Prompt Engineering and Generative AI (30 credits)

The rise of Generative Artificial Intelligence (AI) is undoubtedly one of the biggest technological breakthroughs in modern times. It is provoking a major rethink in many fundamental aspects of our lives including the balance of work between humans and machines, and the foundational knowledge, skills, and abilities that modern humans ought to learn and develop in the era of Generative AI. This course aims to equip you with the crucial skill of prompt engineering and working with Generative AI for efficient task automation, focusing on the art of communicating effectively and systematically with Large Language models to automate tasks and optimise the outcomes. This course begins with a comprehensive introduction to Generative AI, laying the groundwork for an in-depth exploration of prompt engineering. It starts by building a solid understanding of neural networks, deep learning principles, and fundamental Natural Language Processing (NLP) concepts, essential for effectively engaging with AI models. As the course progresses, it delves into Generative AI models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). However, the heart of the curriculum lies in comprehending the ground-breaking capabilities of the Transformer architecture, the foundation of modern Large Language Models. You’ll develop the theoretical framework and the practical skills necessary to craft creative tailored prompts for Large Language Models and develop automation pipelines, enabling you to exploit these powerful tools for problem-solving and automation across various industries.

Through practical exercises, you’ll explore topics like the design and development of Large Language Models from scratch, working with state-of-the-art Large Language Models, formulating systematic prompts for optimal interaction with Large Language Models, Working with APIs, task automation, end-to-end data pipelining, as well as content creation. The course also highlights the ethical considerations of using Large Language Models, emphasizing the need for responsible prompt engineering to mitigate biases and maintain fairness in AI applications.

Advanced Time Series Analysis (15 credits)

The module will provide an introduction to emerging techniques allowing data scientists and practitioners to study and investigate nonlinear time series. For example, we will learn how to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces, we will analyse how to detect causality, long-range correlations, define and detect stability in complex systems.

This module will offer a collection of tools designed to dive deep down into underlying structures of data, allowing future data scientists to detect whether stochastic or deterministic dynamics most likely drive observed complexity. In other words, this module will teach how to become 'data detectives ‘accumulating hard empirical evidence supporting their modelling approach.

The teaching material is organised in a way to support graduate students to be familiar with the basic concept of nonlinear dynamics and become operational in nonlinear time-series analysis (NLTS) paving the way for NLTS to be adopted as conventional empirical toolbox in their future studies or professional practice. The approach to the subject will be highly interactive, with hands-on computer experiments in R code directing the students through NLTS methods and helping them understand the underlying logic.

Research Methods (15 credits)

This module helps to prepare a postgraduate student for undertaking research. Its purpose is twofold: to introduce you to the discipline of research and, at the same time, to help lay the essential foundations for a dissertation of MSc quality. Student interests, time constraints and other practical considerations usually set, and limit, the topic, the research approach and study, and the selection of suitable method(s) appropriate for a MSc dissertation. The module therefore includes: - a consideration of research design issues; - an introduction to research skills; - an evaluation of alternative research methods. Topics covered include, for example, research planning and design, alternative research methods, use of the Internet, research analysis and effective time management. During the module, you’ll define an area of study that could or will form the basis of your research project. You’ll be expected to undertake an appropriate and critical review of the available literature and other information relevant to the proposed project. If a laboratory-based project is envisaged, you will need to give consideration to the instrumentation needed, provide experimental design criteria, at least for the first stages of the proposed work, and take account of all health and safety regulations and appropriate ethical considerations. If necessary, you will need to prepare a formal application for ethical approval of your work.

Modules are subject to change and availability.

Assessment

We'll assess you in several ways including time-constrained assessments, coursework assignments, presentations and a project.

Our dissertation project and module case studies assess your ability to analyse situations, identify key issues, select, synthesise and apply techniques and skills from different modules, and evaluate the appropriateness of solutions when compared to industrial practice.

The dissertation artefact will be based on a real-world scenario.

How you'll study

Our Applied Artificial Intelligence MSc is studied 100% online.

You’ll study through Canvas, our world-class online Learning Management System (LMS), which can be accessed from your phone, PC or tablet at home or on the move. Canvas provides instant access to study materials, forums, and support from tutors and classmates, as well as enabling easy submission of your assignments.

On successful completion of your studies, you’ll be invited to attend a graduation ceremony on campus. If attending the ceremony in person is not possible then we’ll arrange to have your certificate sent to you.

97% of our alumni surveyed found studying via distance learning either very or quite accessible*

Supported distance learning

We understand that distance learning is different to traditional campus study and if you’re new to online study you may have concerns or apprehensions about studying your applied artificial intelligence master's program, and that’s natural.

To help put your mind at ease we have a dedicated Distance Learning Support Team to help and support you throughout your time at ARU, starting with your first online induction and staying with you right through to graduation. In addition, you’ll also be supported by specialist tutors who are experienced in supporting distance learning students and will provide you with the support you need throughout your studies.

Once you start your online masters in AI, we encourage the creation of online communities and many of our learners find these connections with others invaluable, helping them to stay motivated, share concerns or make new friendships.

Contact us to talk through any questions or concerns or visit our support page for more information about the support services available.

Our support services

Be part of the University of the Year

We're proud to be the Times Higher Education (THE) University of the Year 2023.

The prestigious THE awards honour ’exceptional performance during the 2021-22 academic year, and reflect ARU’s success in delivering high-impact projects during this period, despite the challenges of the Covid-19 pandemic.

The award recognises the difference we make in the region and our communities – while also acknowledging the broader impact of our world-leading research, and the contributions our students and graduates make to society.

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Careers

92% of our alumni surveyed felt ‘much more’ or ‘more’ confident at work*

What could an Applied Artificial Intelligence MSc do for my career?

The MSc Applied Artificial Intelligence course is designed in response to the high demand of experts in AI, machine learning, data mining, big data analysis and modelling from different domains, from life sciences to geography, from psychology to finance and banking.

This postgraduate course is ideal for you if you wish to shift the focus of your expertise and career in the direction of AI. You may already have an undergraduate computer science related degree and/or work in the technology industry and wish to specialise in this expanding topic to advance your career. Alternatively, this master’s degree is also open to career switchers who do not have a computer science degree but want to move their career direction to AI. The approach of the course is designed to benefit students with no prior programming experience and enables them to understand the underlying concepts, principal components, design elements of computer programming, one of the most desirable skills for employers.

The subjects chosen as part of the curriculum reflect the modern trends and market demand in Artificial Intelligence. Each module will give you a glimpse into the workings of the IT industry and presents them with topics that prepare you for entering the job market.

What job roles can I consider with an Applied Artificial Intelligence MSc?

Potential job roles that Applied Artificial Intelligence MSc graduates may consider include:

  • AI technologist
  • Data analyst
  • Data scientist
  • Business analyst for data-driven business strategies

The types of sectors that you could be employed in vary and can include healthcare, human resource, marketing, retail, e-business technologies, IT system development and design, IT project management, amongst others.

If you are looking for more information about these job profiles and potential salary earnings see Prospects.ac.uk. It’s important to note that salaries can vary widely depending on the job role, employer, industry sector and location.

What skills will I get from an Applied Artificial Intelligence MSc course to help my career?

Choosing to study an Applied Artificial Intelligence MSc degree will give you many transferable skills. Here are just some of them:

  • Plan and manage a data-driven project through its life cycle, and to reflect on the
  • outcomes
  • Work independently and self-sufficiently
  • Take responsibility and implement your own continuing professional development
  • Conduct research at master’s level

What can I study after an Applied Artificial Intelligence MSc?

Depending on your long-term goals and interests you may decide to explore a doctorate qualification. Having a master’s degree under your belt is a key entry requirement in opening doors to study at this level.

Careers Advice Service

Once you become an ARU student you will be able to access our Employability service to help you at whatever stage of your career, whether that’s landing your dream job or the next progression step.

We offer:

  • careers advice, including one-to-one online and telephone appointments with our experienced advisers
  • help with your CV, job searches, applications and interview preparation
  • an online portal packed with useful careers resources
  • our Employability Programme, which helps you hone the skills employers say they want in graduates.

Entry requirements

  • Applicants will normally hold a first or second class first degree in any subject.
  • Candidates with other degrees, including foundation degrees, but with relevant work experience may also be considered. You will have normally worked for over 3 years in a professional environment regardless of the discipline. You will be expected to attend an interview where an assessment will be made to determine your ability to succeed at postgraduate level.
  • If English is not your first language, you will be expected to demonstrate a certificated level of proficiency of at least IELTS 6.5 or equivalent English Language qualification as recognised by ARU. You’ll need at least 5.5 in each of the four skills - listening, speaking, reading and writing.
  • As a distance learner, you'll also need a suitable computer with internet connection, together with sufficient IT competence to make effective use of our online Learning Management System (LMS) with high-speed internet and email.

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 contact us for further information.

Tuition fees and funding options

Fees

The full tuition fee for our Applied Artificial Intelligence MSc is £8,200.

The tuition fees you pay each year for the online masters in AI will be £2,734. The course is studied over 3 years.

Accredited Prior Learning may reduce the tuition fees. This will be confirmed once your application has been submitted.

Funding

Your course can be fully funded by the new Postgraduate Student Loans now available (subject to eligibility).

We offer payment by instalments, so you can spread the cost of studying with us.

For military students: You can use your ELCs towards this course. Anglia Ruskin University is a recognised ELCAS provider (number 1007). Please contact your Learning Centre for details of ELC, eligibility and how to apply.

For more information on how you fund your studies please see our funding page.

92% of our alumni surveyed said all the effort and expense was worth the outcome* 


What our students say

David Conroy - ARU Student
The flexibility of the online learning format is particularly beneficial for those balancing professional and personal commitments. The support from the teaching staff and the extensive online resources provided by the university creates a robust learning environment. 
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Cat - ARU Distance Learning Student
It has broadened my knowledge and changed my approach to problem solving. It has pushed me out of my comfort zone to learn new things and look at issues through a different lens. 
Read Cat's story
Beth - ARU Student
To be able to delve into a subject that you are passionate about and to explore different topics within that subject was really motivating and helped inform my practice.
Read Beth's story
Professor Sleator - ARU Distance Learning Student
I would recommend ARU in a heartbeat. The material is at the cutting edge, the tutors are at the top of their game, and the support is second to none. 
Read Professor Sleator's story

Next application deadline:

28th June 2024

Discover what you're made of.Apply now before the application deadline.

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*2023 alumni survey with 158 respondents