MSc Advanced Computer Science with Artificial Intelligence

Key facts

  • Start date: September
  • Study mode and duration: 12 months full-time
  • Guest lectures from leading employers and expert alumni

Study with us

Studying an MSc in Advanced Computer Science with Artificial Intelligence at the University of Strathclyde, you'll be learning at an award-winning academic institution - the only University to have won the Times Higher Education University of the Year award twice (2012/2019).

  • an advanced Masters degree for graduates with significant programming experience and a degree in computer science or a related discipline
  • choose from a broad range of optional modules to match your interests and career aspirations

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Why this course

Artificial Intelligence (AI) is found in all aspects of modern society, from the technologies that underpin the economy and how we work, to recreation, healthcare and even the natural environment. Its potential applications are growing daily.

Our MSc in Advanced Computer Science with Artificial Intelligence has been designed to meet the growing worldwide demand for skilled computer science professionals who have expertise in artificial intelligence.

Throughout our MSc you'll:

  • develop an understanding of how artificial intelligence algorithms and technologies are designed, developed, optimised and applied to meet business objectives
  • learn to use a range of software systems that can be used to build reliable, scalable and quality artificial intelligence solutions, and how to apply rigorous AI methodologies through experimental design and exploratory modelling
  • study the ethics of artificial intelligence applications and how different applications require different AI technologies
  • develop your expertise in many of the most widely used artificial intelligence techniques and applications
  • have the opportunity to choose from a wide range of optional modules to tailor your Masters degree to meet your academic interests and career aspirations.

Graduates of the programme will have acquired advanced problem analysis and evaluation skills and the ability to evaluate and communicate the results of artificial intelligence interventions to non-expert stakeholders. These are key skills to enable a successful career as an AI practitioner.

Concept of a futuristic computer made of small cells600x600

THE Awards 2019: UK University of the Year Winner

What you’ll study

On our Advanced Computer Science with Artificial Intelligence Masters, you’ll study two 11-week semesters, each with three or four modules.
Each module typically has four hours of lectures, laboratory practicals and/or tutorials. Additional study time is required to enhance and apply your understanding of the topics covered, through further reading, self-directed study and assignments.

In the summer, between June and August, you’ll undertake an in-depth three-month research project.

A range of software systems are used to build reliable, scalable and quality solutions, and you will apply rigorous methodologies through experimental design and exploratory modelling. For example:

  • the deep learning module allows you to use popular APIs such as Keras and Tensorflow
  • topics such as reinforcement learning for real-world “game” play e.g AlphaStar
  • you'll become familiar with a number of different cloud NoSQL systems and the Map-Reduce programming paradigm
  • evolutionary computation techniques such as genetic algorithms, genetic programming and neural networks will be introduced to a range of financial applications

Research project

Our project supervision programme ensures a named academic is attached to every MSc student during their project study between May and August each year.

Regular meetings are scheduled during the period of the project work, and where an external organisation is involved, meetings between supervisor, student and external body are standard. View all our academic experts in the Department of Computer & Information Sciences.

We've extensive experience supporting students in placement and are well-equipped to manage both university and placement provider expectations.


The course will involve some extensive laboratory-based instruction and student work. The Computer and Information Science department utilise their own specialist laboratories to provide practical student tuition on large scale advanced applications.

The University library also has a sufficient body of resources to support this course.

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Course content

Select 30 credits from the elective modules below, across semesters 1 and 2. Please note, elective modules are subject to change.

Compulsory modules

Legal, Ethical and Professional Issues for the Information Society (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. 

Reasoning for Intelligent Agents (20 credits)

By the end of the module you should understand:

  • classifications of problem features for autonomous systems
  • common approaches to deliberate reasoning for intelligent agents
  • the challenges in integrated planning and execution, and some approaches to solving them
  • how to build systems in the Robot Operating System

Deep Learning Theory and Practice (20 credits)

By the end of the module you should be able to:

  • understand the challenges in the training of deep neural network and how to overcome the challenges with few shot learning techniques
  • understand how to process sequential data with deep neural network and the applications in natural language processing
  • develop understanding of the potentials and generalisation of deep neural networks through theoretical analysis
  • understand deep generative models and their main approaches
  • understand how to build Deep Neural Networks in Python using packages such as Keras/PyTorch and implement such networks

Quantitative Methods for Artificial Intelligence (10 credits)

By the end of this module you should:

  • understand many statistical techniques used in modern AI: basic data description (EDA), significance tests, statistical distributions, 'classical' and Bayesian inference, etc
  • understand and be able to apply probability theory to common problems in modern AI: randomness, probability distributions, variance and expectation, expected values, etc
  • appreciate how techniques from linear algebra are used in modern AI: scalars, vectors, matrices, tensors

Elective modules

Advanced Topics in Software Engineering (20 credits)

This module aims to: 

  • make you aware of key aspects of current software engineering research 
  • familiarise you with the state-of-the-art in terms of what problems can be solved and what are the current exciting challenges 
  • equip you with the necessary skills to allow you to contribute to the software engineering research community 
  • equip you with the skills and background to appreciate the contributions to software engineering research across the full range of material presented at the key international conferences in the field 

Big Data Tools & Techniques (20 credits)

The objectives of the module are to:

  • understand challenges posed by big data, as they refer to its modelling, storage, and access
  • be familiar with a number of different big data technologies and techniques, in terms of efficiency and scalability while also addressing design trade-offs and their impacts
  • 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 impacts
  • be familiar with the Map-Reduce programming paradigm

Information Retrieval (10 credits)

You'll learn to:

  • critically examine a number of influential information-seeking models
  • provide an understanding of research methodologies for studying human information behaviour
  • examine important concepts, such as relevance, in the context of information seeking and retrieval
  • examine how findings from information seeking theory and practise can inform the design of information access systems
  • outline the theory and technology used to construct modern Information Retrieval and Access systems
  • critically evaluate the assumptions behind the evaluation of Information Retrieval systems

Compulsory modules

Research Methods (10 credits)

This module aims to provide you with an understanding of both quantitative and qualitative research processes and associated techniques, including the effective presentation of findings in accordance with the best principles of scholarship.

Deep Learning in Visual Computing Applications (10 credits)

This module will cover an in-depth application of neural networks including convolutional neural networks (CNN), recurrent neural networks (RNN) and deep neural networks. Using these methods, the module will also explore topics such as visualisation of features, model explainability, object detection, image segmentation and image generation.

Game Theory and Multi-Agent Systems (10 credits)

This module consists of about two-thirds game theory and about one-third reinforcement learning, although the two topics blend into each other. We'll also touch on some other points of contact between game theory and AI.

Elective modules

Mobile Software & Applications (20 credits)

By the end of the module you should be able to:

  • appreciate and explain the problems associated with mobile software environments
  • identify and explain the models and techniques typically employed in the design and development of a range of software for mobile environments, and appreciate the limitations of these
  • appreciate the role and impact of context-awareness and persuasion in modern mobile applications
  • demonstrate the ability to implement selections from a range of the software typically used in mobile environments

Distributed Information Systems (20 credits)

This module will give you an extended understanding of the deep, technical issues underlying information systems, in the particular context of distributing content over the world-wide web.

AI for Finance (20 credits)

This class 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.

Fundamentals of Machine Learning for Data Analytics (10 credits)

After completing this module you'll be able to:

  • understand the aims and fundamental principles of machine learning;
  • understand a range of the essential core algorithms and approaches to machine learning;
  • apply the algorithms covered on substantial data sets using Python and Scikit-learn and interpret the outcomes;
  • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations.

Business Analysis (10 credits)

This module aims to provide tools and techniques for the effective analysis and design of business information systems and enable you to develop an understanding of their respective advantages, disadvantages and applicability.

Evolutionary Computing for Finance (20 credits)

On completion of this module you'll:

  • gain an understanding of a range of evolutionary computational and machine learning techniques
  • gain an understanding of the relative advantages and disadvantages of each technique for different financial applications
  • be able to evaluate the results of a financial problem investigated using evolutionary computation and machine learning techniques

60 credits

Over the summer semester, from June until August, you'll undertake a significant piece of work that will be your MSc project.  This is typically a very practical activity, and can include:

  • analysing a problem and designing, implementing and evaluating a solution
  • conducting an in-depth experimental analysis of technique or technology
  • performing an analysis of a large data set, and building and evaluating models of the data

We'll also have a range of projects available that are sponsored by our staff based on their latest research, and many that come direct from the employers we work with, representing real-world problems they are trying to solve.

Learning & teaching

Our teaching and learning methods include lectures, tutorials, laboratory practicals and combinations of individual and group work. These will not only develop your expertise in computer science and artificial intelligence, but also in communication, team-working and analytical skills which are all essential skills for your future career.

Class material will be placed on Myplace for students to gain access to in their own time. A personal computer (microcomputer, desktop computer, laptop computer, tablet) is required to access teaching materials and attending online lectures/meeting. The computer lab is also available for students to use.

You’ll have regular contact with our expert staff, many of whom as well as being leading international researchers have been nominated for, and won, teaching awards based on nominations by our students.


Assessment is through a combination of individual work, group work, exams and practical work in laboratories. Around half the classes are assessed entirely by coursework, the others are a combination of coursework and examination.

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Entry requirements

Academic requirements / experience
  • minimum second-class (2:2) honours degree or international equivalent in computer science or a closely related discipline
  • significant programming experience, preferably in an imperative programming language e.g. Java, Python etc. The majority of modules will use Python
  • other disciplines who have significant programming experience should contact us to discuss applying for this course
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 tuition for students whose IELTS scores are below 6.0. 

As a university, we now accept many more English language tests other than IELTS for overseas applicants, for example, TOEFL and PTE Cambridge. View the full list of accepted English language tests here.

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-UK/Ireland) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde.

Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.

Please note: Previous Maths & English qualifications and your undergraduate degree must meet GTCS minimum entry requirements as well as the pre-Masters course and an interview will be conducted before an offer can be made.

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Fees & funding

All fees quoted are for full-time courses and per academic year unless stated otherwise.

Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.

All fees are in £ sterling, unless otherwise stated, and may be subject to revision.

Annual revision of fees

Students on programmes of study of more than one year (or studying standalone modules) should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.

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England, Wales & Northern Ireland




Available scholarships

Take a look at our scholarships search for funding opportunities.

Additional costs

If you are an international student, you 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.

International students

We've a thriving international community with students coming here to study from over 140 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

How can I fund my course?

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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.

Don’t forget to check our scholarship search for more help with fees and funding.

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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.

Don’t forget to check our scholarship search for more help with fees and funding.

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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.

Don’t forget to check our scholarship search for more help with fees and funding.

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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.

Don’t forget to check our scholarship search for more help with fees and funding.

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International students

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.

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

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Graduates will have developed the expertise to pursue a career directly in the applications of artificial intelligence, a field where skilled graduates are well-paid and in high demand. You can look forward to careers designing and building the digital technologies that underpin the global economy and, indeed, every aspect of human activity from recreation to healthcare to business.

Artificial Intelligence skills are also becoming increasingly important across multiple industries and graduates will be well equipped to pursue a career in a multi-disciplinary setting where a combination of their existing skills, artificial intelligence knowledge and analytical skills will be required.

Throughout the programme there will be opportunities for you to meet industry employers and take part in recruitment events, in addition to taking advantage of a wealth of support offered by our careers service.

We have a dedicated careers fair for our computer science and information management students each year, with over 30 leading companies, including:

  • Amazon
  • JP Morgan
  • Morgan Stanley
  • British Telecom
  • Bridgeall
  • Kana
  • Skyscanner
  • ThinkAnalytics

Example roles

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 have a range of needs for which they need AI professionals. This can include to do things like deploy state of the art AI chatbots, developing image classifiers, creating computer vision applications or planning for autonomous vehicles. This degree provides you with the core skills to be able to deliver these needs.
  • Data Scientist: 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.
  • Business/policy analyst: as a business/policy analyst you will identify improvements which can be made to organisational systems using artificial intelligence, write specifications for their modification and enhancement, and be involved in the design of new IT solutions to improve business efficiency.
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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.

Please ensure you upload evidence to support your programming experience; degree transcript and/or detailed CV if your degree is over five years old.

Start date: Sep 2024

Advanced Computer Science with Artificial Intelligence

Start date: Sep 2024

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Contact us

PGT Admissions Team

Telephone: +44 (0)141 553 6023