Harness the power of data-driven innovation with AI
University of Gloucestershire’s 100% online MSc Artificial Intelligence equips you with the in-demand skills to design, develop, and apply intelligent systems that drive innovation and business growth. You’ll explore the core principles of artificial intelligence and machine learning while learning how to implement real-world solutions in areas such as decision-making, prediction, speech recognition, image processing, and enhancing human capability.
Throughout the course, you’ll focus on the strategic use of data and algorithms to solve complex challenges across a range of industries, including healthcare, automotive, manufacturing, IT, and eCommerce. You'll also gain practical experience in evaluating and applying the latest tools and technologies to deliver impactful results in dynamic and fast-changing environments.
This MSc is designed to support your professional ambitions while accommodating your personal commitments—preparing you for a successful career in AI, machine learning, and the wider field of computer science.
Course overview
This course gives you the skills to build intelligent systems that can process visual, textual, and sensory data. Study the algorithms behind facial recognition, natural language understanding, and machine learning to create tools that solve real-world problems.
You’ll investigate how machines interpret images, recognise speech, and respond to language—technologies that power everything from autonomous vehicles to chatbots. As you build your knowledge of deep learning, neural networks, and data-driven modelling, you’ll also consider the ethical and societal implications of deploying AI at scale.
Skills and knowledge
Graduates of this course will be able to:
Solve computing problems by evaluating and identifying effective solutions
Apply architecture knowledge to assess design trade-offs and constraints
Evaluate computer vision methods like filtering and edge detection
Implement AI algorithms and tools to solve defined problems
Create a conversational AI agent for question answering tasks
Entry requirements
To study Gloucestershire’s 100% online MSc Computer Science with Artificial Intelligence, you will need to have completed:
A 2:2 undergraduate honours degree, or a comparable professional qualification. (Applicants with significant work experience but without a formal Level 6 qualification will also be considered.)
OR
A minimum of three years of relevant work experience (CV required, including one reference).
English language requirements
If English is not your first language, or if your most recent education or work experience was not conducted in English, you must demonstrate proficiency through an approved English language test. This includes IELTS (minimum overall score of 6.0, with no individual component below 5.5) or an equivalent qualification.
Modules
Principles of Programming Languages
Explore the evolution of programming languages and the core abstractions they use, including data types and control structures. This module introduces key paradigms through practical examples from languages like Java and Lisp, while also covering advanced features such as exceptions and polymorphism. You will gain a foundational understanding of how programming languages are structured and the rationale behind their design.
Artificial Intelligence and Machine Learning
This module on Artificial Intelligence & Machine Learning offers an in-depth exploration of advanced AI methodologies and their applications in real-world problems. After this introduction, students will evaluate various data and knowledge representation techniques to identify optimal solutions, and compare different types of reasoning to enhance system effectiveness. The course emphasises hands-on experience in developing AI technologies such as expert systems, genetic algorithms, fuzzy logic, and probabilistic reasoning, alongside machine learning models including deep learning, artificial neural networks (ANNs), and convolution neural networks (CNNs). Finally, you will also tackle ethical considerations involved in the deployment of AI systems in business and personal contexts, assessing potential impacts and mitigation strategies.
Computer Vision
Computer Vision (CV) is an important field of computer science, specifically Artificial Intelligence. The goal of Computer Vision is to learn how to teach computers to interpret the human world. The computer needs to process, analyse and interpret the human world. However, humans need to teach it how to. In this introductory module, we will learn what computer vision is and the different types of computer vision, such as image and object processing and recognition. We will look into different areas of applications, such as manufacturing and healthcare, and how we can boost productivity and cut costs.
Natural Language Processing
Learn about modern natural language processing (NLP) concepts, tools and applications. More specifically, you will learn about and understand how morphological, syntactic and semantic analysis of text are carried out in natural language processing (NLP) applications. Further, you will also be introduced to the different methods and tools that can be used to implement NLP systems for applications such as sentiment analysis, question answering and machine translation. This module will also cover how NLP fits in machine learning by touching upon areas such as classifications, artificial neural networks, and model training.
Algorithms & Data Structures
Learn to design and evaluate the algorithms and data structures that form the backbone of effective software development. This module focuses on analysing efficiency, understanding core data objects such as arrays, stacks, queues, trees, and graphs, and implementing algorithmic patterns. You'll also explore techniques for enhancing code performance, reusability, and optimisation to support scalable software solutions.
Computer Architecture
Examine how computer systems are designed and how their architecture supports business and technical needs. You’ll explore hardware and software components, system functionality, and decision-making strategies for implementing performance-optimised, usable solutions. By the end of the module, you'll be able to assess architectural designs aligned with organisational requirements.
Operating Systems
Gain a comprehensive understanding of how operating systems function, including process and memory management, file systems, device drivers, and concurrency. This module explores both design principles and practical implementations across systems like Windows, iOS, Linux, and distributed networks. Case studies will help you evaluate OS choices and their implications for system design and software development.
Computer Network Security
Develop a solid foundation in the principles and practices of network security. You’ll analyse vulnerabilities in LANs, WANs, databases, and operating systems while exploring risk assessment, intrusion detection, encryption, authentication, and access control. This module equips you to design secure systems and apply the right security measures for diverse communication environments.
Machine Learning
This module is an introduction to machine learning with R. The students will learn how to process and analyse various data sets. Topics covered include exploratory data analysis (EDA), unsupervised learning (e.g., PCA, k-means), and supervised learning (e.g., linear regression, logistic regression). Machine learning techniques such as test-train-split, k-fold cross-validation, and regularisation will also be covered. By the end of the module, students will be able to write code to apply machine learning techniques and methodologies mentioned above to real data sets.
Research Methods for Computer Science
Develop your ability to conduct academic research in computer science. You’ll explore research design, methodology, and epistemological frameworks, while learning how to critically review literature, develop a proposal, and ensure ethical compliance. The emphasis is on creating a robust foundation for a future research project or dissertation.
Applied Research for Computer Science (30 credits)
Apply your research knowledge to a real-world computer science problem by developing a thesis or project. Building on your research methods training, you'll define a problem, design and execute a research strategy, collect and analyse data, and present solutions or insights, demonstrating your ability to conduct independent, applied research.
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