- Start date: September
- Study mode and duration: 12 months, full-time
MSc conversion: No need for computer science as first degree
Study with us
Studying a Masters in Artificial Intelligence & Applications at the University of Strathclyde, you'll be learning at an award-winning academic institution - the only to have won Times Higher Education University of the Year award twice.
Our AI & Applications Masters is designed specifically for graduates without a computing science background. It's a course in modern artificial intelligence, with a focus on intelligent agents and machine learning.
Artificial intelligence and machine learning skills are in wide demand. You'll gain skills to get ahead of the AI-driven transformation in our economy and society.
You may also be interested in:
Why this course
Our MSc Artificial Intelligence & Applications is a conversion degree designed specifically for graduates without a computing science background.
It's based on the Office for Artificial Intelligence’s National AI Strategy recommendations for AI Masters courses. You'll learn not just core AI-techniques, but how to place them within a business context so as to show their value.
A skills shortage, particularly around machine learning, means graduates are in high demand. This degree is designed to give you the skills to get ahead of the AI-driven transformation in our economy and society. You'll gain transferable skills to prepare you for a professional career in AI and its applications. These are expected to cover all aspects of society both within the tech sector, as well as outside, including:
Hear from Dr John Levine about our MSc Artificial Intelligence degree:
We've created this MSc to give you the skills you'll need to work with big data, deep learning and techniques for creating powerful autonomous systems.
Dr John Levine, senior lecturer
What you'll study
The focus of our Artificial Intelligence & Applications Masters programme is to equip you for the job market. At the end of the course you'll be able to:
- understand how AI algorithms, technologies and methodologies are designed, developed, optimised and applied at scale to meet business objectives
- select the appropriate statistical methods for sampling, distribution, assessment, bias and error
- understand AI problem structuring methods, and evaluate which methods are appropriate for a particular problem
- apply rigorous AI-methodologies through experimental design, exploratory modelling, and hypothesis testing to reach robust conclusions
- explain how such conclusions are reached to internal and external stakeholders
- understand how to extract data from systems, and ensure standards of data quality and consistency for processing by AI-systems
- integrate separate data sources in order to produce AI-solutions to meet user needs
- understand and make use of different types of data models
- understand how to build scalable machine learning technologies, and optimise them, to improve performance
- understand a range of software systems to build reliable, reusable, scalable AI solutions to time, quality and budget
- demonstrate why AI solutions meet user requirements to stakeholders and show the value gain associated to a given solution
- understand the range of applications of AI and how different application areas require different AI-technologies
Network with a range of employers
As a student within the Department of Computer & Information Sciences, you'll have opportunities to develop your profile as a computing professional and to network with a range of employers including:
- JP Morgan
- Morgan Stanley
- Goldman Sachs
- members of our Industrial Advisory Board are important contributors to our curriculum development and contribute via guest lectures
- depending on your specific interests, visits to Strathclyde's City Observatory or the Fab Lab will illustrate practical applications of computing
- employers and students interact at our IT Careers Fair arranged during the academic year
- our MSc dissertation projects are industry-focused and often industry-sponsored, with real-world topics; students are also encouraged to present or publish MSc findings at conferences
- our Careers Service will support your career planning through development sessions in topics such as interview skills, CV development and presentation skills
Dr Michael Cashmore (course coordinator)
My research centres on using AI Planning for the control of Autonomous Systems that act robustly and safely in dynamic and uncertain environments. I have experience building architectures for deliberative control of robot systems, and in modelling discrete-continuous problems with non-linear dynamics using constraint programming. My current interests lie in building autonomous systems that can act within a team of AI agents and humans, can communicate and react to teammates, and collaborate towards a shared goal.
My primary research interests lie in the application of AI search-based strategies and machine learning techniques to software engineering problems; for example, to generate program test data or automatically detect software system failures, and I am also looking at the converse problem of testing AI systems. Additionally, I have experience and interests in using machine learning in a variety of contexts such as forecasting buyer behaviour, predicting building energy performance, modelling interventions to combat sedentary behaviour, and predicting health outcomes.
I work in human-centric AI to investigate collaborative decision making between AI and humans. The focus is to make AI more understandable and explainable through inferring causality and compositionality, and to investigate new approaches in human and AI interactions.
Much of my research takes place at the intersection of informatics and the life sciences; I have worked extensively with data-driven models of disease and host-parasite dynamics in both human and animal populations. My group explores novel machine learning approaches that can integrate data-driven and expert-derived knowledge to support disease diagnosis across multiple animal species, using domain ontologies. In addition, as the associated mobile apps must function in multiple African languages, often with limited textual corpora, we have interests in emerging transfer learning approaches from NLP to support such ‘minority’ languages.
My interest is in building Autonomous Adaptive and Self-Learning Multi-Agent Systems. Furthermore, I'm interested in the development and the use of Artificial Intelligence techniques with special focus on game-based learning, applications for education, and robotics.
Chat to a student ambassador
If you want to know more about what it’s like to be a Science student at the University of Strathclyde, a selection of our current students are here to help!
Our Unibuddy ambassadors can answer all the questions you might have about courses and studying at Strathclyde, along with offering insight into their experiences of life in Glasgow and Scotland.Chat now!
The MSc Artificial Intelligence & Applications is a conversion degree designed specifically for graduates without a computing science background.
The curriculum comprises 180 credits, with one credit being equal to ten hours of student learning. The curriculum includes 60 credits in Semester 1, 60 credits in Semester 2, and a 60-credit project that typically runs from May to August.
Legal, Ethical & Professional Issues (10 Credits)
This module aims to ensure that you're aware of the legal, social, ethical and professional issues commensurate with the practice of Information Systems Engineering.
On completion of the module, you'll be able to:
- appreciate the characteristics of professionalism as it relates to modern data management
- recognise and appreciate the professional aspects of other modules in the course, and how those aspects influence practice
- form a sound basis on which you'll subsequently be able to practise Information Systems Engineering with a due regard for legal, ethical and social issues
Quantitative Methods for AI (10 Credits)
The aim of this module is to provide you with the foundations of mathematics that are required to understand modern Artificial Intelligence techniques. The module will focus on three topics: probability, statistics and linear algebra.
On completion of the module you'll be able to:
- understand and apply probability theory as used in modern AI:
- probability distributions
- variance and expectation
- expected value
- understand and apply statistical techniques as used in modern AI:
- basic data analysis
- significance tests
- Bayesian inference
- understand and use linear algebra techniques as used in modern AI:
Big Data Technologies (20 Credits)
This module aims to give you an understanding of the challenges posed by big data, an understanding of the key algorithms and techniques which are embodied in data analytics, and exposure to a number of different big data technologies and techniques.
After completing this module, you'll be able to:
- understand the fundamentals of Python to enable the use of various big data technologies
- understand how classical statistical techniques are applied in modern data analysis
- understand the potential application of data analysis tools for various problems and appreciate their limitations
- be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impact
AI for Autonomous Systems (20 credits)
This module focuses on implementing AI algorithms and building autonomous systems. This involves gaining an understanding of what Artificial Intelligence means in the context of autonomous systems, such as the key algorithms and techniques that enable rational decisions.
On completion of the module you'll be able to:
- program in Python, with the goal being to implement key AI algorithms and build AI systems
- define and understand the problem of Artificial Intelligence as it relates to autonomous systems
- apply search techniques to enable autonomous systems to choose actions that are appropriate to their goals
- apply key techniques to adversarial problems, such as Mini-Max and Monte-Carlo Tree search
Deep Learning & Neural Nets (20 Credits)
The most impactful area of AI has been machine learning using neural networks. When combined with reinforcement learning, these neural networks can also become autonomous agents that can, for example, learn to play games to an extraordinarily high standard. This module will cover these two areas of AI.
After completing this module, you'll be able to:
- define and understand the problem of agents that learn
- understand how Deep Reinforcement Learning uses Deep Neural Networks together with Reinforcement Learning in, for example, the Atari Games work of Google Deepmind
AI for Finance (20 credits)
This module provides an overview of the application of AI techniques - including those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) - to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.
After completing this module, you'll be able to:
- understand the benefits and opportunities for evolutionary computing in the context of financial applications
- understand the principles of evolutionary computation, in particular genetic programming and genetic algorithms, as well as neural networks (particularly those configurations most suited to time series data)
- understand how the computational approaches covered in the class may be applied to financial problem-solving and understand their limitations
- develop and evaluate practical solutions to finance-based problems
Machine Learning for Data Analytics (20 credits)
This module equips you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The module balances a solid theoretical knowledge of the techniques with practical application via Python.
After completing this module, you'll be able to:
- understand the aims and fundamental principles of machine learning
- understand the applicability of the algorithms to different types of data and problems, along with their strengths and limitations
- understand and apply a range of the advanced algorithms and approaches to deep learning and machine learning using artificial neural networks and interpret the outcomes
Dissertation (60 Credits)
You'll undertake an individual project under supervision, which should contain an element of original research. The project will be AI-application based (i.e. analysing, specifying, building and evaluating an AI-application or demonstrator, and forming recommendations and conclusions on the relative merits of the technologies involved and the methodologies used). The project will include production of developed code, supporting written documentation, and practical demonstration. The project is assessed through a written dissertation.
Learning & teaching
Each module is delivered through a combination of lectures, practical computer laboratory work, and tutorials. Module content is also made available online in our virtual learning environment, including recorded lectures and interactive exercises.
The course includes optional support sessions provided by our Careers Service, such as Marketing Yourself delivered in Semester 1. In addition, the course includes workshops for non-technical skills, covering topics including research and study, critical-thinking and problem-solving, and Developing Effective Study Skills.
Each module is assessed by combination of coursework and exam. The course includes both individual and group coursework assignments.
The University of Strathclyde is a great place to be as it combines a top-notch educational system mixed with amazing uni-life. It is everything and more I can ever think of for a university.
This programme is designed for graduates without a computing science background. If you have a computing science background, we recommend you apply for one of the following Advanced Computer Science pathways as you will not be made an offer for this programme:
Minimum second-class (2:2) Honours degree or overseas equivalent.
|English language requirements|
You must have an English language minimum score of IELTS 6.0 (with no component below 5.5).
We offer comprehensive English language courses for students whose IELTS scores are below 6.0. Please see details of our English language teaching.
As a university, we now accept many more English language tests in addition to IELTS for overseas applicants, for example, TOEFL and PTE Cambridge. View the full list of accepted English language tests here.
While it's not a prerequisite, you'll find some basic practice with Python to be helpful during the first semester of the course.
"Artificial Intelligence: A Modern Approach", 4th US ed. by Stuart Russell and Peter Norvig provides a great overview of artificial intelligence and is the recommended textbook for more than one module. It's available in the University Library.
Pre-Masters preparation course
The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.
Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.
We've a thriving international community with students coming here to study from over 100 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.Visit our international students' section
Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
|England, Wales & Northern Ireland|
International students may have associated visa and immigration costs. Please see student visa guidance for more information.
Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.
How can I fund my course?
Scottish postgraduate students
Scottish postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.
Students coming from England
Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Students coming from Wales
Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Students coming from Northern Ireland
Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.
We've a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.
AI graduates are highly employable. They can look forward to well-paid professional careers designing and building the digital technologies that underpin the global economy and, indeed, every aspect of human activity from recreation through healthcare to business and the natural environment.
Job market analysis shows that the most in-demand key skills include big data, machine learning and neural networks, all of which are central to this degree. Example roles could include:
- AI professional: businesses generate huge amounts of data every day and all want to clean that data, understand that data, extract information from that data and turn that information into information to drive the business forward. This degree gives you exactly the skills needed to perform these roles
- Software developer: as a software developer you'll be playing a key role in the design, installation, testing and maintenance of the AI-technologies that are set to transform the world. Your programmes will be the key driver for the success of business
- Business/policy analyst: as a business/policy analyst you will identify improvements which can be made to organisational systems using AI, write specifications for their modification and enhancement, and be involved in the design of new IT solutions to improve business efficiency
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based right in the very heart of Glasgow. We're in the city centre, next to the Merchant City, both of which are great locations for sightseeing, shopping and socialising alongside your studies.Life in Glasgow
Shortly after graduating, I had the opportunity of working with a robotics company in the UK which uses state-of-the-art technologies for robotic fruit and vegetable packing as their Machine Learning Engineer. Many of the skills I learned at Strathclyde were directly applicable on the job and I was able to integrate very quickly within the team.
There is currently no deadline for submitting applications. However, we encourage you to apply early as we consider applications on a first come, first served basis, and may introduce an application deadline due to high demand.
This programme is designed for graduates without a computing science background. If you have a computing science background, we recommend you apply for one of the following Advanced Computer Science pathways as you'll not be made an offer for this programme:
Start date: Sep 2022
Artificial Intelligence and Applications
Have you considered?
We've a range of postgraduate taught and Masters courses similar to this one which may also be of interest.