Artificial Intelligence MSc
Why choose this course?
Study a masters in this rapidly evolving field. You'll gain a strong foundation in AI technologies and their application in the real world.
Modules are designed to align with the growing market opportunities in various areas, including big data, data mining, machine learning, deep learning and their direct application in Computer Vision, Natural Language Processing, and Cyber Security.
| Mode | Duration | Start date |
|---|---|---|
| Full time | 1 year |
January 2025 September 2025 |
| Full time | 2 years including professional placement |
January 2025 September 2025 |
| Part time | 2 years |
January 2025 September 2025 |
Please note: Teaching on this course may take place on more than one KU campus.
| Main Location | Penrhyn Road |
Reasons to choose Kingston University
- Gain skills in an area that is in demand, both in industry and in the public sector.
- Tailor your degree to your interests, with optional modules in specialist areas of AI.
- Through a project dissertation, you will study an area of interest in depth. This can offer valuable industry experience.
What you will study
Year 1
Professional placement
Core modules
Applied Data Programming
30 credits
This module emphasises a practical and applied approach to programming and software skills for data scientists which differs from typical software engineering approaches in that the emphasis is on the use and manipulation of data using languages and platforms designed for use in real-life, data-driven problems. The languages and platforms are considered only as far as their use for data manipulation are needed with limited exploration of underlying theory or data structures. This prioritises practical implementation including locating, accessing, loading, manipulating, securing, storing and describing data, and enables the introduction of aspects of data analysis, data-mining and machine learning provided by the chosen languages and platforms.
Big Data and Data Mining
15 credits
This module is designed to equip you with the knowledge and know-how necessary to harness the potential within larger datasets. It explores the characteristics of extremely large and complex datasets, unravelling their potential for uncovering patterns, trends, and correlations through the application of advanced data mining methods, and their utility for discovery of patterns, trends and correlations using data mining methods.
You are first introduced to the diverse sources of big data and the crucial aspects of data quality and trustworthiness. This is then followed by an examination of Big Data technologies tailored to handle unique characteristics specific to various domains.
By adopting a holistic approach that combines theoretical foundations with practical applications, you will emerge equipped to confidently navigate the multifaceted landscape of big data analytics while remaining mindful of social, legal, and ethical frameworks.
Ethics and Regulation of Artificial Intelligence
15 credits
This module explores regulation and ethical issues that Artificial Intelligence (AI) raises. It considers AI in the context of its use. Current regulation will be explored along with examples of its application in different contexts. Ethical theory will be introduced and discussed using examples of protocols put in place in areas where regulation is lacking.
Machine Learning and Deep Learning
30 credits
This module introduces fundamental concepts and methods in Classical Machine Learning and Pattern Recognition as well as Neural Networks and Deep Learning. You will firstly be introduced to classical methods, before being taught modern approaches. You will then be exposed to applications related to your course. The module is taught in a practical fashion and therefore some knowledge of a programming language is required.
Project Dissertation
60 credits
This module constitutes the major individual piece of work of the masters programme where you will carry out a project involving independent critical research, design and implementation (where applicable).
Optional modules (choose 2)
Computer Vision
15 credits
This comprehensive module provides you with a deep insight into the principles and applications of computer vision. The module begins with an exploration of image processing fundamentals and algorithmic basics, gradually progressing to advanced topics such as generative and discriminative elements through various techniques and models used for tasks like image classification, object detection and 3D vision.
Throughout the module, you will engage in hands-on projects, applying their knowledge to real-world scenarios. The emphasis on cutting-edge techniques includes convolutional neural networks, adversarial machine learning, transfer learning, and addressing emerging trends in object detection and image segmentation.
By the module's conclusion, you will possess a versatile skill set, well-equipped to tackle challenges in diverse fields such as healthcare and robotics.
Cyber Security and Artificial Intelligence Applications
15 credits
This module explores the intersection of artificial intelligence (AI) and cybersecurity, focusing on the application of cybersecurity principles to mitigate threats and vulnerabilities in AI-powered systems. You will gain an understanding of the unique challenges posed by AI in cybersecurity contexts and develop strategies for effectively defending against cyber threats while considering ethical, security, and privacy implications.
Natural Language Processing
15 credits
This module aims to provide you with a grounding in the various aspects of, and approaches to, Natural Language Processing (NLP) by computers, and the essential principles thereof, plus familiarise you with some technologically important applications of NLP.
Professional placement
Professional Placement
120 credits
The Professional Placement module is a core module if you're following a masters programme that incorporates an extended professional placement. It provides you with the opportunity to apply your knowledge and skills in an appropriate working environment, and develops and enhances key employability and subject specific skills in your chosen discipline. You may wish to use the placement experience as a platform for a major project or your future career.
It is your responsibility to find and secure a suitable placement opportunity; this should not normally involve more than two placements which must be completed over a minimum period of 10 months and within a maximum of 12 months. The placement must be approved by your Course Leader prior to commencement to ensure its suitability. You will have access to the standard placement preparation activities offered by the Student Engagement and Enhancement (SEE) group.
Read more about the postgraduate work placement scheme.
Artificial Intelligence MSc offers a comprehensive range of modules that cover cutting-edge algorithms, tools, and techniques in the field of AI. These modules are designed to align with the growing market opportunities in various areas, including big data, data mining, machine learning, deep learning and their direct application in Computer Vision, Natural Language Processing, and Cyber Security.
Modules are structured in a way that follows the typical roadmap of an AI project, ensuring a systematic approach to learning. Elective modules allow you to specialise and focus on specific areas of interest.
Work placement scheme
Many postgraduate courses at Kingston University enable students to take the option of a 12-month work placement as part of their course. Although the University supports students in finding a placement and organises events to meet potential employers, the responsibility for finding the work placement is with the student; we cannot guarantee the placement, just the opportunity to undertake it. You may find securing a professional placement difficult as they are highly competitive and challenging, but they are also incredibly rewarding. It is very important to prepare and apply yourself if this is the route you wish to take. Employers look for great written and oral communication skills and an excellent CV/portfolio. As the work placement is an assessed part of the course, it is covered by a student's Student Route visa.
Find out more about the postgraduate work placement scheme.
Entry requirements
Our modern teaching environment at Kingston University
There is a wide range of facilities at our Penrhyn Road campus, where this course is based. You will have access to a modern environment with the latest equipment, including:
- dedicated postgraduate computing laboratories, fully-equipped with fold-flat LCD screens, data-projection systems and high-spec processors
- industry-standard development software and tools, such as Python, Scikit, Learn and Tensorflow
- the learning resources centre, offering subject libraries, online database subscriptions and resource materials.
Our dedicated team of IT technicians supports the labs and is always on-hand to provide assistance.
Resources in London
Kingston is just a 30-minute train journey from central London, where you can access a wealth of additional libraries and archives, including the British Library and the Institute of Engineering and Technology.
Course fees and funding
Here you can find more details about fees for this course, as well as any funding opportunities available to you for this course. Please note that fees relate to the academic year in question and will increase in future years.
If you require a Student Route visa to reside in the UK you may not be able to enrol on a part-time programme at the University.
Kingston University has carefully considered the Student Route visa and has decided not to offer Student Route visa part-time study. Student Route visa sponsorship is only available to students studying on a full-time course.
Additional costs
Depending on the programme of study, there may be extra costs that are not covered by tuition fees which students will need to consider when planning their studies. Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, access to shared IT equipment and other support services. Accommodation and living costs are not included in our fees. Where a course has additional expenses, we make every effort to highlight them. These may include optional field trips, materials (e.g. art, design, engineering), security checks such as DBS, uniforms, specialist clothing or professional memberships.
After you graduate
You will be well equipped to work in a variety of industries that are rapidly adopting automation and robotics technology, such as manufacturing, healthcare, logistics, and transportation. You will also be able to contribute to cutting-edge research in the field of robotics.
Course changes and regulations
The information on this page reflects the currently intended course structure and module details. To improve your student experience and the quality of your degree, we may review and change the material information of this course. Course changes explained.
Programme Specifications for the course are published ahead of each academic year.
Regulations governing this course can be found on our website.
