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Artificial Intelligence PgDip (Distance Learning)

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Start Dates

6 September 2027, 17 January 2028, 8 May 2028

Duration

2 years part-time


Recent Awards For Excellence

Computer Science & Information Systems - QS 2025
Find out more about these awards
About this course

Overview

Why choose Huddersfield for this course?

  • Explore hybrid AI systems: how AI learns from data and how it reasons with knowledge.
  • Develop advanced skills to design, implement and evaluate intelligent systems responsibly and ethically.
  • Taught 100% online, part-time allowing you to study while working.

Accreditation and Professional Links

Recognised connections to give you an extra edge when you graduate. Read More

Our 100% online Postgraduate Diploma (PgDip) is designed for graduates with a background in computer science or relevant professional experience who want to develop advanced expertise to design, implement, and critically evaluate intelligent systems that underpin modern technological innovation. 

Our specialist course brings together both data-driven approaches - such as machine learning, deep learning and generative AI - with knowledge-based approaches including automated reasoning, planning, knowledge representation and intelligent control. This integrated approach enables you to understand not only how hybrid AI systems achieve performance, but how their decisions are structured, interpreted, justified and trusted.

You’ll gain a deep understanding of the principles and computational techniques that enable intelligent behaviour in machines. Throughout the course you’ll learn to select appropriate AI methods for complex, real-world challenges, evaluate their performance and limitations, and implement working systems using contemporary AI frameworks and tools such as Python and MATLAB.  

This course aims to prepare you for careers in AI, robotics, autonomous systems, intelligent decision support, and related fields.

Learn more about Distance Learning at Huddersfield.

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 relevant work experience.
  • Other qualifications and/or experience that demonstrate appropriate knowledge and skills at an Honours degree level.

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 with a minimum score of 6.0 in writing and a minimum of 5.5 in any single component or Duolingo English certificate, score 105 or above. 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.

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.

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.

Teaching and Assessment

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

Technology and System Requirements

As a Distance Learning student, you must provide and have access to certain IT equipment and facilities to access your Virtual Learning Environment (VLE) and to fully participate on your course.

Where could this lead you?

Your Career

Graduates of the Artificial Intelligence MSc can progress into a range of data-driven and technical roles across sectors including technology, finance, healthcare, engineering and creative industries. Many go on to work as Data Scientists, Data Engineers, Data Analysts, Software Engineers or Research Scientists, applying AI techniques to solve real-world challenges. There is also demand for specialist leadership positions such as Data Science Manager. As AI continues to transform the global job market, knowledge and experience of AI systems are increasingly valued across many professional fields, opening pathways beyond traditional technology roles. 

Source: Lightcast data extracted from the Graduate Career Explorer 

98% of our postgraduate students go on to work and/or further study within fifteen months of graduating.

* HESA Graduate Outcomes 2022/23, UK domiciled.

£38,500 Average salary of our postgraduate students fifteen months after graduating.

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

My time at Huddersfield was instrumental in advancing my career. The course gave me the confidence and skills to address complex challenges in AI & software engineering. The 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

£6,600

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

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course. 

For detailed information please visit www.hud.ac.uk/distance-learning/fees-and-finance/ 

£6,600

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

Modules credits can range from 15 to 60, dependent on the content of the module. Read more about total credits required for a range of degrees, to allow you to calculate the potential total cost.

Please note that tuition fees for subsequent years may rise in line with inflation (RPI-X) and/or Government policy and/or to ensure our distance learning fees are competitive.

Tuition fees will cover the cost of your study at the University. Read more about what is and is not covered by Tuition Fees including compliance for Goods and Services Tax for International Students studying an online course. 

For detailed information please visit www.hud.ac.uk/distance-learning/fees-and-finance/ 

Flexible payments

Learn how to pay your fees including flexible instalment options

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Funding options

Explore ways to fund your course with exclusive discounts and government loans

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

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