MSc Artificial Intelligence

What you will study

The MSc Artificial Intelligence degree aims to develop graduates with advanced knowledge of intelligent systems. You will study topics such as machine learning, computer vision, natural language processing, intelligent agents, and data analytics, gaining both theoretical understanding and practical skills for developing innovative, ethical, and effective AI solutions.

Modules: 

• Artificial Intelligence: In this module, you will explore the core foundations of artificial intelligence, focusing on how machines can simulate reasoning, learning, and problem-solving. The module covers knowledge representation, heuristic search, inference mechanisms, and supervised and unsupervised learning. You will critically analyse different AI paradigms, evaluate their applications, and understand the trade-offs between symbolic and data-driven approaches. Through practical exercises and case studies, you will apply classical AI methods to real-world challenges, developing insight into how intelligent systems are designed, trained, and deployed responsibly across domains such as automation, robotics, and decision support.
• Intelligent Agents: You will study the design and behaviour of autonomous agents that can perceive, reason, and act within dynamic environments. The module introduces concepts such as reactive and deliberative architectures, agent communication, and reinforcement learning. You will explore multi-agent systems and investigate how cooperation, competition, and coordination emerge in complex environments. Emphasis is placed on modelling adaptive behaviour and decision-making under uncertainty. Practical sessions will help you develop agent-based simulations and reinforcement learning solutions, applying them to real-world contexts such as robotics, autonomous systems, and intelligent decision-support frameworks.
• Computer Vision and Natural Language Processing: You will explore how artificial intelligence enables machines to interpret visual and textual information. This module covers key computer vision and natural language processing concepts, including image recognition, feature extraction, object detection, embeddings, and transformer-based architectures. You will gain practical experience in designing and implementing models that perform visual analysis, classification, and language understanding. By integrating both domains, you will learn how AI systems perceive, interpret, and communicate effectively. The module also examines challenges related to ambiguity, bias, and context, providing a balanced understanding of the opportunities and limitations of perception-driven AI.
• Advanced Data Structures and Algorithms: You will examine advanced methods for organising and processing data efficiently, focusing on algorithmic problem-solving and performance optimisation. The module explores complex data structures such as trees, graphs, heaps, and hash tables, alongside algorithmic design paradigms including divide-and-conquer, dynamic programming, and greedy algorithms. You will learn to analyse algorithmic complexity and scalability, applying these principles to AI-related contexts such as search, optimisation, and data processing. Emphasis is placed on developing robust, efficient, and ethical computational solutions to real-world problems where algorithmic efficiency and precision directly impact AI system performance.
• Advanced Machine Learning: You will gain an in-depth understanding of modern machine learning algorithms and their applications in artificial intelligence. The module explores deep learning architectures, ensemble methods, and probabilistic models, while examining how models learn from and generalise to complex data. You will critically evaluate approaches to feature learning, regularisation, interpretability, and scalability. Emphasis is placed on practical implementation, allowing you to experiment with model tuning, validation, and evaluation using realistic datasets. By the end, you will be able to design and assess advanced learning systems capable of addressing a wide range of AI-driven challenges.
• Research Methods for Digital Technologies: You will develop the ability to conduct rigorous and ethical research within the field of computing and artificial intelligence. The module covers literature review techniques, quantitative and qualitative methodologies, experimental design, and data collection strategies. You will also explore statistical analysis, critical evaluation of research findings, and effective academic writing and presentation skills. Ethical considerations, research integrity, and data management are integral components. This module prepares you to plan and execute independent research, equipping you with the methodological and critical-thinking skills necessary for your MSc dissertation or future academic and professional research projects.
• Dissertation: You will undertake an independent research project exploring a specialised area of artificial intelligence. Guided by an academic supervisor, you will define a research problem, conduct an in-depth literature review, and design an appropriate methodology to address it. The project may involve experimental development, applied investigation, or theoretical exploration. You will collect and analyse data, critically evaluate findings, and reflect on the implications for AI practice and research. The dissertation provides an opportunity to demonstrate originality, methodological rigour, and advanced technical competence while contributing new insights to the field of artificial intelligence. 
  

Entry requirements & applying

The entry requirements for this course is an honours degree of 2:2 classification in any subject area. 

Teaching & Assessment

Teaching  

The computing program suite employs a diverse range of cutting-edge industry tools and software, complemented by innovative teaching methods. This dynamic approach not only imparts industry-relevant skills but also empowers you to elevate your work to new heights when possible.

Assessment  

Assessments in MSc Artificial Intelligence at university level are designed to evaluate your understanding, application, and proficiency in various aspects of the discipline. These assessments encompass a diverse range of methods, including:

• Coursework and Projects: Assignments and projects provide hands-on experience, allowing you to apply theoretical knowledge to real-world scenarios. This may include software development projects, research papers, or problem-solving tasks.
• Coding Assignments: Practical coding assignments assess your programming skills, logical reasoning, and ability to develop efficient and effective code.
• Group Projects: Collaborative projects evaluate teamwork, communication, and the ability to work in diverse teams, reflecting the collaborative nature of the tech industry.
• Presentations: You may be required to present your findings, solutions, or project outcomes, enhancing your communication and presentation skills.  
  
• Laboratory Work: Practical sessions in computer labs assess your ability to apply concepts, troubleshoot issues, and work with various tools and technologies.
• Problem-solving Exercises: These exercises challenge you to solve complex problems, encouraging critical thinking and analytical skills.
• Reports and Documentation: Writing reports or documenting project processes assesses your ability to communicate technical information clearly and concisely.

Personalised Support       

The department follows a well-established open-door approach, actively interacting with students, alumni, and industry stakeholders. Essential information and communication avenues are facilitated through tools like Teams and Moodle. Additionally, every student is assigned a personal tutor, fostering regular meetings, while additional personalised support is extended to part-time students through the Virtual Learning Environment (VLE). 

Teaching and Learning

We offer workshops and support sessions in areas such as academic writing, effective note-making and preparing for assignments.

Students can book appointments with academic skills tutors dedicated to helping deal with the practicalities of university work. Our student support section has more information on the help available.

In terms of particular needs, the University’s Inclusion Services can provide appropriate guidance and support should any students require reasonable adjustments to be made because of a recognised prevailing disability, medical condition, or specific learning difference.

Career prospects

Our dedicated Careers and Employability team is committed to helping you achieve your professional goals. They provide personalised advice, useful resources, and extracurricular employability events to prepare you for the job market.

Graduates of this course can pursue careers in: 

• Artificial Intelligence Engineer
• Machine Learning Scientist
• Data Scientist / Data Analyst
• Computer Vision Engineer
• Natural Language Processing Specialist
• Intelligent Systems Developer
• Research Scientist (AI / Robotics)
• AI Consultant or Architect
• Software Engineer (AI & Automation)
• Academic or Industrial Researcher

Fees & funding

You do not have to pay your tuition fees upfront.

The fees you pay and the support available will depend on a number of different factors. Full information can be found on our fees & finance pages. You will also find information about what your fees include in the fee FAQs.

All fees are subject to any changes in government policy, view our postgraduate fees.

International

This course is open to international students, for information about the university’s entry requirements for EU/international students, please visit our international section. 

Apply Now

Please contact enquiries@wrexham.ac.uk to request an application form.