• Postgraduate

Artificial Intelligence MSc

Overview

Overview

If you would like to be at forefront of the ongoing technological revolution then our MSc Artificial Intelligence course is for you.

On this course you will gain both theoretical and practical knowledge to work across disciplines and implement AI systems where they are needed.

Artificial intelligence (AI) is redefining the way we live, allowing us to automate processes and enhance our quality of life. The resulting new technologies create the need for trained experts with a deep understanding of their intricacies and applications.

As well as learning the technical skills, you will have the chance to explore realistic applications through group and individual projects.

We have contacts with major technology companies, perfect for opportunities within industry-initiated and healthcare-related projects.

working on laptop

Select your desired study option, then pick a start date to see relevant course information:

Study options:
We support flexible study by offering some of our courses part-time or via distance learning. To give you real world experience before you graduate, we also offer some courses with a placement or internship. All available options are listed here. Your choices may affect some details of your course, such as the duration and cost per year. Please re-check the details on this page if you change your selection.

Start date:

If your desired start date is not available, try selecting a different study option.

Why study Artificial Intelligence with us?

Why study Artificial Intelligence with us?

What our students say…

The best part of the course is that you can explore topics of your interest and go in-depth with projects that advance your skill set. Finishing the course not only gave me more practical tools, but it also taught me to ask questions that, hopefully, will help me change the world for the better. 

Karoline Lundberg, BYBI Design Executive
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There are seven labs and studios in the School
This course has contacts with major technology companies
Tile: Mac and PC Labs
Industry focused teaching
Course detail & modules

Course detail & modules

AI has broad application in a variety of industries. This course is directed by an Industrial Advisory Panel that meet twice a year to ensure that it provides the right mix of hands-on skills and up-to-date knowledge suitable for to the wide variety of applications that this field addresses.

On this course, you will:

  • develop a systematic understanding of the AI domain
  • gain in-depth knowledge of AI techniques and develop intellectual skills to create well-justified solutions
  • develop a comprehensive understanding of the associated research and professional skills necessary in practice
  • develop expertise to plan design and implement appropriate AI solutions in practical scenarios
  • gain substantial practical experience, and learn to prepare for the future challenges of AI
  • gain a solid foundation for applied research in artificial intelligence
  • prepare for work in a rapidly evolving and technologically diverse environment.

The degree provides you with the skills to meet the needs of the industries that recognise AI's transformative potential, from healthcare to manufacturing to the automotive industry.

Theories and fundamentals of AI and machine learning will be taught in both lecture and workshop formats.

You will be supported in your learning and personal development through participation in industry and research seminars, group discussions and presentations, and coursework.

Additionally, you will receive hands-on experience accessing equipment and facilities within the School of Computing and Engineering.

Supporting modules

Learning and Professional Development (for international students only)

Programming Support


Compulsory modules

  • Artificial Intelligence

    This module will introduce you to the fundamentals of AI with a focus on

    1. the structures, resources and processes that together make up an intelligent agent
    2. the techniques, models and tools that can be used to simulate the “intelligent” processes
    3. the skills and capabilities necessary to critically review AI literature and/or products, to synthesise ideas, to systematically solve AI problems and to communicate effectively.

    You will gain the knowledge and skills required to understand the fundamentals of AI, to solve real world problem more “intelligently”, and ultimately to build intelligent artefacts.

    Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including mathematical optimization, logic, probability, and many others. AI has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

  • Machine Learning

    Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

    This module familiarises you with some basic machine learning algorithms and techniques and their applications, as well as general questions related to analysing and handling large data sets. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus on machine learning algorithms and applications, with some broad explanation of the underlying principles.

  • Deep Learning

    This module provides an introduction to deep learning. You will learn the fundamentals, models, algorithms, implementation and recent progress of deep learning, and obtain hands-on experience on training neural networks.

    You will start with exploring artificial neural networks (ANNs) and its operation, gradually moving onto exploring the approaches to develop large ANNs with many layers, also called deep neural networks (DNNs).

    This is followed by the understanding of how deep learning works and approaches to train the networks covering the technical know-how such as vanishing/exploding gradient problems, activation function and training optimiser, which is often known as “deep learning”.

  • Responsible AI

    The module will enable you to focus on current ethical issues in AI and undertake an investigation into how to resolve them in future. You will learn current framework in place for development of ethical AI solutions and will be asked to apply those principles.

    You will be able to specialise in societal, legal and ethical impact of AI and learn skills required to design these systems.

    The module content is divided to three parts:

    • Responsible AI in Design – to ensure full awareness of consequences for people by development choices
    • Responsible AI by Design – to understand the behaviour of AI systems and the integration of ethical reasoning as part of the algorithms
    • Responsible AI for Designers – to learn the codes of conduct and the current standards that ensure the integrity of developers.
  • Research Methods

    This module will ensure that you are fully prepared to undertake applied research at master’s level. You will be able to pursue your research ideas and back them up with appropriate data and statistics.

    The assessment of the module will prepare you for the delivery of a dissertation proposal. The topics covered in the module include:

    • introduction to research
    • research process and developing research proposal
    • developing research objectives, choosing research methods, presenting & analysing data, and making
      conclusions
    • building a literature review
    • research methodologies in computing
    • research ethics
    • writing dissertation proposal
    • writing dissertation, managing references and using document tools
    • identification and use of subject related library resources; Understanding plagiarism
    • developing a career plan.
  • Dissertation

Optional modules

  • Big Data Analytics

    Big data is a fast-growing field and skills in the area are some of the most in-demand today. Big data technologies cover a range of architectures, frameworks and algorithms designed to handle very large and often highly complex datasets.

    The module will enable you to understand big data, its applications and associated issues for storing, managing, processing and analysing massive amounts of datasets, as well as become familiar with software tools and frameworks underpinning big data analytics.

    You will also acquire the knowledge of statistical, mathematical and machine-learning techniques, and develop the ability to design and implement big data analytics modelling and applications to real-world problems.

  • Computer Vision

    This module will explore the advanced principles and techniques currently being used in real-world computer vision systems, and the research and development of new systems.

    Computer vision lets computers gain high-level understanding from digital images or videos, and seeks to automate tasks that the human visual system can do. It has become ubiquitous with applications in search, image understanding, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, segmentation, localisation and detection.

    In this module you will learn how digital images are formed, how they are represented and stored on computers, and how they can be processed by computers to extract semantic information. You will have the opportunity to develop algorithms for detecting interesting features in images, design convolutional neural networks to perform tasks such as image classification, and explore techniques for solving real-world problems such as object detection. Relevant programming language (e.g. Python, OpenCV, MATLAB, etc.) will be used for model developments.

Entry requirements

Entry requirements

You should have an honours degree (2:2 or above) from a UK university or equivalent in computing or other STEM-based disciplines, with a significant level of computing and programming. 

We will also consider equivalent professional qualifications or a relevant HND provided you can demonstrate relevant work experience. In this case you will have to complete an interview with your application and some optional modules may not be available without a first degree in computing. All applications are considered individually. 

Find out more about our processes for recognising previous experience

We look for students who show enthusiasm and a passion for the subject through previous study or professional experience.

If you have any questions about the relevance of your qualifications or experience please contact the course leader shown in the teaching staff.

6.5 IELTS or above

You need to meet our English language requirement of 6.5 overall score for IELTS, with a minimum of 5.5 for each of the 4 individual components (Reading, Writing, Speaking and Listening). Visit our English language requirements page for information on other English language tests we accept.

You also need academic qualifications at the same level as UK applicants. In some countries where teaching is in English, we may accept local qualifications. Check for local equivalents.

We offer pre-sessional English language courses if you do not meet these requirements. Find out more about our English Language courses.

We look for students who show enthusiasm and a passion for the subject through previous study or professional experience.

If you have any questions about the relevance of your qualifications or experience please contact the course leader shown in the teaching staff.

Teaching staff

Teaching staff

Neda Azarmehr is smiling in front of some green trees

Dr Neda Azarmehr

Neda has been lecturing at the undergraduate and postgraduate levels, covering a wide range of courses in Computer Science. Neda is also actively contributing to the Intelligent Sensing and Vision research Lab which has fostered interdisciplinary research by providing a framework for cross-faculty collaborative working.

Neda has been lecturing at the undergraduate and postgraduate levels, covering a wide range of courses in Computer Science. Neda is also actively contributing to the Intelligent Sensing and Vision research Lab which has fostered interdisciplinary research by providing a framework for cross-faculty collaborative working.

Study & career progression

Study & career progression

Student working on her laptop

AI has broad application in a variety of industries. The degree provides you with the skills to meet the needs of the industries that are recognising the transformative potential of AI, from healthcare to manufacturing to the automotive industry.

In its Industrial Strategy, the UK Government has outlined AI and data revolution as one of its four Grand Challenges, to ensure the UK leads the way for the industries of the future. By embedding AI across the UK, the Government aspires to drive the economic growth.

Artificial intelligence (AI) and machine learning are redefining the way we live and work, allowing us to automate processes and enhance productivity. These new technologies offer a multitude of opportunities for skilled engineers with an understanding of their applications and intricacies.

You could also apply to advance your studies with an MPhil or PhD, either at UWL or another institution.

How to apply

How to apply

News

Important notes for applicants

Disclaimer

*Modern universities - defined as higher education institutions that were granted university status in, and subsequent to, 1992.

**The National Student Survey 2022 and 2023 - Based on an average of all 27 questions. Excludes specialist institutions.

Testimonials - our students or former students provided all of our testimonials - often a student from the course but sometimes another student. For example, the testimonial often comes from another UWL student when the course is new.

Optional modules - where optional modules are offered they will run subject to staff availability and viable student numbers opting to take the module.

Videos - all videos on our course pages were accurate at the time of filming. In some cases a new Course Leader has joined the University since the video was filmed.

Availability of placements - if you choose a course with placement/internship route we would like to advise you that if a placement/internship opportunity does not arise when you are expected to undertake the placement then the University will automatically transfer you to the non-internship route, this is to ensure you are still successful in being awarded a degree.