Curious about studying MSc Artificial Intelligenceat University of Stirling? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
MSc - Master of Science
University of Stirling
Full Time
Sep 2027
1 Year
The UK AI market is worth more than £16.9 billion, according to the US International Trade Administration. It’s expected to grow to £803.7 billion by 2035. The number of UK AI companies has increased by 688% over the last 10 years, showing rapid expansion. Today, one in six UK organisations uses at least one type of AI technology (source: Global Legal Insights 2025). Launch your career in AI Our MSc in Artificial Intelligence is designed for students from diverse academic and professional backgrounds who want to advance their careers in AI-focused fields. This course explores the theory of a wide variety of AI-related techniques, including: machine learning and deep learning; mathematics and statistics for data science; programming using Python; computer vision and natural language processing. You’ll also gain hands-on experience with the tools and methods used to apply these concepts to real-world challenges. AI internships opportunities We designed this artificial intelligence Masters degree in collaboration with employers to ensure you're equipped with the skills needed for a successful career in AI. The course includes opportunities for internships with technology providers. Past internship placements have included organisations such as Aggreko, Falcon Foodservice, Diageo, and Synaptec. Engage with industry Our academics are engaged in world leading research in AI fields such as complex systems modelling, computer vision, machine learning, and optimisation. We’re a member of The Data Lab Innovation Centre, developing data science talent and skills required by industry. Studying an artificial intelligence Masters at Stirling provides a fantastic opportunity to build your professional network with fellow students and industry experts. You’ll engage with industry through guest lectures and local career events. In the last couple of years, we've had talks from Huawei, Bigspark, Red Star (AI for healthcare), Virtonomy, KBC Group, and Leonardo UK. Gain practical AI experience On our Artificial Intelligence MSc, you get the opportunity to work with academics on ongoing commercially relevant research projects. You can also take part in projects with industry or technology providers. Past projects included companies such as Craft Prospect, OPEX, ST Microelectronics R&D (UK) Ltd, Dukosi Ltd and Canon Medical Research Europe. Watch Rishil's story; For students interested in a January start, the course duration is 21 months. For example, students starting in January 2026 will graduate in November 2027. This structure allows for flexible learning and the opportunity to develop other skills during the summer period when teaching is not scheduled.
Data Science is a rapidly emerging discipline at the intersection of computer science, statistics, and application domains. The main goal of data science is to extract knowledge and insight from data, which can then be turned into positive action. This module introduces the fields of Data Science and Big Data. It covers the fundamental steps of the data science process, as well as some specific techniques and case studies. The module begins with a high-level discussion of important principles and study the conceptual steps of the data science process, then moved to an exploration of time series analysis and an applied case study. The module is assessed by a coursework with a specific data science case study and demonstration of the techniques covered in the practical sessions.
Deep learning-based computer vision (CV) and natural language (NLP) have emerged as major driving forces in AI-based technology development. Usage scenarios include a range of applications such as robotics, autonomous driving, medical imaging, remote sensing for environmental monitoring, security and surveillance and many more. This module will present the understanding of the deep learning-based CV and NLP algorithms that are core to many real-life examples.
The application of machine learning has exploded in recent years. Classifying loan application risk, predicting when machines are likely to fail, classifying spam, and recommending products to customers are just a few examples. This module covers the analysis of structured (numbers) and unstructured (text/images) data using a number of advanced machine learning methods. The module is geared towards teaching an understanding of the methods of processes of applying data analytics to real world problems.
This module introduces you to some of the foundational mathematical and statistical methods used to analyse quantitative and qualitative information from small to large data sets. We will use the R statistical programming language. We give an introduction to some of the fundamental concepts and results in linear algebra and probability theory. You will also cover the statistical tests you may use on real world data, with no expectation that you have any prior statistical knowledge, or mathematical experience at undergraduate level. First, you will learn how to explore a data set so you can provide an initial description, understanding the difference between quantitative and qualitative data.
Knowing how to write scripts is essential to everyone who would work with data. This module introduces you to the use of the programming language Python for manipulating data. Python is one of the most popular scripting languages used to manipulate and represent data.
In recent years, the amount of data gathered in all aspects of our lives has increased exponentially, from businesses exploring customer behaviour to public health bodies monitoring infection rates. This increased volume of data has increased the need for graduates who can analsyse this data, with skills dependent on the nature of the data. The first half of the module will give an understanding of basic statistical methods to the analysis and interpretation of large data sets. You will learn: how to: make an initial exploratory analysis of data using descriptive statistics; perform introductory parametric statistical tests; use multiple linear regression techniques. You will also develop an understanding of appropriate statistical techniques to use when analysing large data sets.
Optimisation is defined as “the action of making the best or most effective use of a situation or resource”. Decisions are made everyday, in every industry, based on incomplete information and limited resources, hence making the right decision is key. Many of the processes that are observed are random, or stochastic, in nature. However, when these problems are represented mathematically, they often take a common form, and it is these forms of problems that this module will allow you to solve.
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