Artificial Intelligence MSc

Select the year

2025-26

2026-27

2027-28

Start Dates

21 September 2026, 11 January 2027

Duration

1 year full-time

Recent Awards For Excellence

Computer Science & Information Systems - QS 2025

Find out more about these awards

About this course

Overview

Artificial intelligence underpins some of the most complex and critical technologies in use today. Our Artificial Intelligence MSc is designed for graduates with a background in computer science or relevant professional experience who want to develop advanced, specialist expertise in this rapidly evolving field.

You will explore how intelligent systems are designed, implemented and evaluated, gaining a deep understanding of the principles and computational techniques that enable machines to learn, reason and make decisions. The course brings together data-driven approaches such as machine learning, deep learning and generative AI with knowledge-based techniques including automated reasoning, planning, knowledge representation and intelligent control. This integrated approach enables you to understand not only how AI systems achieve performance, but how their decisions are structured, interpreted, justified and trusted.

Throughout the course, you will develop the ability to select appropriate AI methods for complex real-world problems, critically assess their performance and limitations, and implement robust solutions using contemporary AI frameworks and programming languages such as Python and MATLAB. You will also engage with key issues around interpretability, explainability, ethics and responsible AI. A substantial individual project allows you to apply your knowledge to a realistic research or development challenge, preparing you for advanced professional roles in AI or for doctoral study.

We also offer this course as a part-time Distance Learning route.

Career opportunities after the course *

Data Scientist

Machine Learning Engineer

Data Engineer

Software Engineer

Data Analyst

*Lightcast

Who can apply?

Entry Requirements

Entry requirements for this course are normally:

A BSc or BEng Honours degree (2:2 or above) in Computing or Engineering or related subject or an equivalent professional qualification. For applicants who received their degree more than 10 years ago, we will need evidence to demonstrate current knowledge in the subject area, such as reference letters, training certificates or significant work experience.
Other qualifications and/or experience that demonstrate appropriate knowledge and skills at an Honours degree level.
Substantial (3 years) relevant industry experience.

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent. Read more about the University’s entry requirements for students outside of the UK on our International Entry Requirements page.

What will you learn?

Course Details

Knowledge Graphs (KGs) are part of the broader field of knowledge representation and reasoning (KR), an area of artificial intelligence concerned with how computer systems represent, manipulate and reason about information. KGs power systems such as semantic search, product recommendations and knowledge-based insights used by leading technology companies. This module introduces you to the principles, models and tools used to build and apply knowledge graphs. You will learn how to design and automate graph structures, populate them with real-world data, and query them to uncover new insights. You will also explore knowledge graphs interact with graph neural networks, large language models, and other AI methods to support reasoning, prediction and generative applications. Through practical work, you will gain experience in creating intelligent, graph-based solutions that apply core KR ideas to modern AI challenges.

This module explores how intelligent agents can plan and reason about actions to achieve complex goals. You will trace the evolution of automated planning, from the early STRIPS (STanford Research Institute Problem Solver) formalism to modern approaches that combine symbolic reasoning, heuristics, and learning. Through hands-on engagement with planning engines and frameworks, you will learn how to represent problems, generate and evaluate plans, and apply state-of-the-art techniques such as hybrid and hierarchical planning. By the end of the module, you will be able to analyse, compare, and discuss contemporary research and real-world applications of AI planning in areas such as robotics, logistics, and autonomous decision-making.

In this module, you will explore how Artificial Intelligence (AI) is applied across real-world domains to solve complex problems and drive innovation. You will study key areas such as computer vision, natural language processing, large language models, chatbots, autonomous systems, and intelligent decision-making. You will learn how AI technologies are integrated into systems used in industry, public services, and research, while considering their capabilities, limitations, explainability, robustness, and ethical implications. Through practical examples and case studies, you will discover how AI is shaping sectors including healthcare, finance, transport, supply chains, and the creative industries. By the end of this module, you will understand how to connect theoretical concepts of AI with their real-world applications and appreciate the impact of AI on society and the future world of work.

Machine Learning (ML) techniques power many of today’s most transformative technologies, from intelligent assistants and recommender systems to autonomous vehicles and medical diagnostics. This module will introduce you to the key principles and algorithms of ML, both as independent systems and as integral components within broader AI frameworks. You will develop a strong conceptual and practical understanding of how machines can learn from data and experience to recognise patterns, classify information, make predictions, and optimise decisions. You will also explore how to select suitable learning methods for different problem types, analysing the strengths, limitations, and trade-offs between classical and modern approaches. Topics include data-driven learning methods such as tree-based models, ensemble learning, kernel methods, clustering techniques, artificial neural networks and their real-world applications. Through hands-on practical sessions, you will work with widely adopted machine learning tools and frameworks to develop a practical understanding of how core algorithms operate in practice, including data preparation, model training, performance evaluation, and bias analysis.

This module explores how deep learning enables modern generative AI systems that can create realistic images, text, and other complex data. You will begin by developing a strong understanding of deep neural networks, including how they are structured, trained, and optimised to recognise and represent patterns in data such as images and language. Building on these foundations, you will study key generative models including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), diffusion models, and Large Language Models (LLMs). Through hands-on work with contemporary tools and frameworks, you will learn how to design, train, and fine-tune deep and generative models for a variety of applications. The module also encourages critical reflection on the ethical, social, and computational implications of generative AI, preparing you to apply these technologies responsibly and creatively in real-world contexts.

This module explores how artificial intelligence enables robots to perceive their environment, make decisions, and act autonomously. You will learn how core components such as perception, planning, navigation, and learning are designed and integrated within modern robotic architectures. Through hands-on work with simulation tools and frameworks, you will experiment with algorithms that support intelligent behaviour in dynamic environments. The module links theory to practice, showing how AI techniques are applied to develop autonomous systems that can sense, adapt, and interact effectively. By the end of the module, you will be able to critically evaluate approaches to autonomy and understand how software, algorithms, and sensors combine to create intelligent robotic systems.

This module enables you to independently undertake a project based on a problem of your own choosing. Designed to be integrative, the project serves as a culmination of the knowledge, skills, competencies and experiences you've gained throughout your studies, while also encouraging further development in these areas. As the project is student-led, you will take ownership of the project from start to finish — negotiating agreements, managing communications, and collaborating effectively with all stakeholders at each stage. In addition, the module offers a comprehensive overview of the research process, academic writing, and key professional considerations including legal, ethical and social issues. It also supports your career development by exploring employability pathways and professional growth.

Teaching and Assessment

Discover what to expect from your tutor contact time, assessment methods, and feedback process.

Where could this lead you?

Your Career

The top five job titles advertised in the UK for graduate roles associated with Artificial Intelligence MSc courses are: Data Scientist; Machine Learning Engineer; Data Engineer; Software Engineer; and Data Analyst.

Source: LightcastTM data - job postings from December 2023 to December 2024 showing jobs advertised associated with a selection of relevant graduate roles.

98%
Percentage of the University's postgraduate students go on to work and/or further study within fifteen months of graduating.

* HESA Graduate Outcomes 2022/23, UK domiciled.

£38.5k
The average salary of our postgraduates fifteen months after graduating.

* HESA Graduate Outcomes 2022/23, mean salary, UK domiciled, full-time UK employment as main activity.

My time at the University of Huddersfield was instrumental in advancing my career. The course gave me the confidence and skills to address complex challenges in AI and software engineering. Although I was already in the industry, the new skills helped me adapt to emerging technologies and remain at the forefront of my field.

- Marco Dinacci
Artificial Intelligence MSc Graduate Technical Lead at Apple

How much will it cost?

Fees and Finance

£10,305 per year

This information is for Home students applying to study at the University of Huddersfield in the academic year 2026/27.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy.

For detailed information please visit https://www.hud.ac.uk/study/fees/

£18,700 per year

This information is for international students applying to study at the University of Huddersfield in the academic year 2026/27.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy.

For detailed information please visit https://www.hud.ac.uk/international/fees-and-funding/

Optional short field trips e.g. one day, are sometimes also arranged. Previous field trips have included Bletchley Park. The costs of these field trips are heavily subsidised by the school but can sometimes incur a nominal cost and/or deposit of between £5 and £40 depending on the trip.

Scholarships and Bursaries

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What’s included in your fee?

We want you to understand exactly what your fees will cover and what additional costs you may need to budget for when you decide to become a student with us.

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If you have any questions about Fees and Finance, please email the Student Finance Team.

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What does the University offer?

Gallery

See below for a glimpse at some of our facilities

The Spärck Jones Building

The Study Mill

A wide range of computing labs

Robotics Lab

Industy standard equipment

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Artificial Intelligence at Huddersfield

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Watch our on-demand webinar

Explore what it's like to study Computer Science at Huddersfield. Our on-demand webinar introduces you to the academics behind our AI, Cyber Security and Data Analytics postgraduate courses, offers on campus and through Distance Learning.

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Why Hud

Explore the unique opportunities and resources that make our institution a top choice for students seeking a well-rounded and future-focused education.

Explore Huddersfield

Make Huddersfield your home. A Yorkshire town, surrounded by beautiful countryside, with easy access to neighbouring cities; Leeds, Manchester and Sheffield. You’ll find plenty of places to eat, drink, shop and socialise, there’s ambitious plans for future development. Our campus is situated next to the town centre - making your commute a breeze.

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Your Student Experience

Get involved in great opportunities helping you to make friends from all across the world and create lifelong memories. Join one of over 100 societies with the Students’ Union, experience new cultures through global events, earn whilst you learn and build your skills via our Global Professional Award.

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Budget Friendly

Huddersfield is not only one of the friendliest towns, it also offers you a lower cost of living than most big cities. This means you could have more money to spend, making your uni experience even better. On campus you’ll also find plenty of facilities and services to help you budget and make your money go further.

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Sustainability Matters

We’re committed to sustainability and aim to be carbon neutral by 2030. Our plan includes reducing emissions, enhancing biodiversity, and improving wellbeing through sustainable buildings. We’re investing in digital solutions, low-impact travel, and embedding sustainability into teaching and research, investing over £1m in carbon reduction.

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Committed to Your Future

Since 2015, we’ve invested £198 million to create spaces where you can learn, collaborate and thrive. We’re continuously investing in our campus with your wellbeing in mind—adding features that make our buildings healthier, more supportive and better places for you to succeed.

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Careers support

We know you’re coming to university to study on your chosen subject, meet new people and broaden your horizons. However, we also help you to focus on life after you have graduated to ensure that your hard work pays off and you achieve your ambition.

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Student support

At the University of Huddersfield, you’ll find support networks and services to help you get ahead in your studies and social life. Whether you study at undergraduate or postgraduate level, you’ll soon discover that you’re never far away from our dedicated staff and resources to help you to navigate through your personal student journey.

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Teaching Excellence

Great teaching is engaging and inspiring — it helps you reach your full potential and prepares you for the future. We don’t just teach well — we excel — and we have the awards and recognition to prove it.

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Inspiring Academics

Our researchers carry out world-leading work that makes a real difference to people’s lives. Staff within the Department of Computer Science may teach you on this course.

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Research Excellence

You’ll be taught by staff who want to support your learning and share the latest knowledge and research.

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Accommodation

Looking for student accommodation? Huddersfield has you covered. HudLets has a variety of accommodation types to choose from, no matter what your preference. HudLets is the University’s approved accommodation service, run by Huddersfield Students’ Union.

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Further Study

Many of our graduates stay at Huddersfield to complete postgraduate research degrees at Masters or PhD level.

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Legal information

When you enrol as a student of the University, your study and time with us will be governed by our terms and conditions, Handbook of Regulations and associated policies. It is important that you familiarise yourself with these as you will be asked to agree to them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan.

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons:

Changes to a course you have applied for but are not yet enrolled on

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment. We may occasionally have to withdraw a course you have applied for or combine your programme with another programme if we consider this reasonably necessary to ensure a good student experience, for example if there are not enough applicants. Where this is the case we will notify you as soon as reasonably possible and if you are unhappy with the change we will discuss with you other suitable courses we can transfer your application to. If you do not wish to transfer to another course with us, you may cancel your application and we will refund you any deposits or fees you have paid to us.

Changes to your course after you enrol as a student

Changes to option modules

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have an equivalent range of options to that advertised for the course. We will let you know in good time the options available for you to choose for the following year.

Major changes

We will only make major changes to non-optional modules on a course if it is necessary for us to do so and provided such changes are reasonable. A major change is a change that substantially changes the outcomes, or a significant part of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), type of delivery or assessment of the core curriculum. For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators or a commissioning or accrediting body. We may also make changes to improve the course in response to student, examiners’ or other course evaluators’ feedback or to ensure you are being taught current best practice. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff being unable to teach due to illness, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to pandemics, other disasters (such as fire, flood or war) or changes made by the government.

Major changes would usually be made with effect from the next academic year, but may happen sooner in an emergency. We will notify you as soon as possible should we need to make a major change and will consult with affected groups of students and any changes would only be made in accordance with our regulations. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you let us know before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for any additional tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so and you may be eligible for an exit award depending on how far through your course you are.

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed in accordance with the student protection plan.

The Office for Students (OfS) is the principal regulator for the University.

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Artificial Intelligence MSc

Overview

Why choose Huddersfield for this course?

Accreditation and Professional Links

Entry Requirements