2025/26 entry
MSc Artificial Intelligence (Machine Learning)
About this course
A very topical course, combining theory and practical aspects of machine learning with a view to forming capable professionals for the jobs market in this field.
- Embark on this newly developed course on a topic of great recent and predicted growth
- Explore the theory of machine learning and practical applications
- Benefit from studying both the practical focus and real industrial applications - this is one of a small number of such courses available
- Learn from academics with substantial experience in machine learning and industrial collaboration
Machine Learning is the scientific study of the ways in which computer systems can be programmed to perform a specific task without using explicit instructions, relying on patterns and inference instead through algorithms and statistical models.
This course is unique in combining theoretical and practical aspects of Machine Learning that will prepare graduates for a career in Industry or Academia. Modules include both aspects throughout the programme and prepare graduates for a variety of roles in Machine Learning development and deployment.
Fees and funding
There are many ways to fund postgraduate study for home and international students
Fees
The fees quoted at the top of this page cover registration, tuition, supervision, assessment and examinations as well as:
- library membership with access to printed, multimedia and digital resources
- access to programme-appropriate software
- library and student IT support
- free on-campus wifi via eduroam
Additional costs
Although not all of the following are compulsory/relevant, you should keep in mind the costs of:
- accommodation and living expenditure
- books (should you wish to have your own copies)
- printing, photocopying and stationery
- PC/laptop (should you prefer to purchase your own for independent study and online learning activities)
- mobile phone/tablet (to access online services)
- field trips (travel and activity costs)
- placements (travel expenses and living costs)
- student visas (international students only)
- study abroad opportunities (travel costs, accommodation, visas and immunisations)
- academic conferences (travel costs)
- professional-body membership
- graduation (gown hire etc)
Funding
There are many ways to fund postgraduate study for home and international students. From loans to International Scholarships and subject-specific funding, you’ll find all of the information you need on our specialist postgraduate funding pages.
Please be aware that the UK’s departure from the EU may affect your tuition fees. Learn more about your fee status and which tuition fees are relevant to you.
Employability
Further your career prospects
LJMU has an excellent employability record with 96% (HESA 2018) of our postgraduates in work or further study six months after graduation. Our applied learning techniques and strong industry connections ensure our students are fully prepared for the workplace on graduation and understand how to apply their knowledge in a real world context.
As a machine learning graduate, you can expect to be responsible for creating software, algorithms and mechanisms that support intelligent systems, that can learn and develop themselves as they operate. Self-driving cars, pattern recognising predictive systems, for instance, are examples of such systems. Machine Learning medical systems that can recognise patterns to predict health outcomes are becoming increasingly relevant for medical prediction and diagnosis.
You will provide computers with the automatic ability to learn, fine tune and improve performance with their own experience.
In addition, there are huge opportunities in research, both academic and in industry, developing new algorithms, systems and conducting experiments on intelligent systems.
The student experience
Discover life as a postgraduate student at LJMU.
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Course modules
Discover the building blocks of your programme
Your programme is made up of a number of core modules which are part of the course framework. Some programmes also have optional modules that can be selected to enhance your learning in certain areas and many feature a dissertation, extended report or research project to demonstrate your advanced learning.
Core modules
Research Methods
20 credits
The aim of this module is to develop your knowledge of effective and academic research design at Masters level and provide guidance on the purpose and design of literature reviews; the use of theory; writing strategies; citation and ethical considerations. It provides an understanding of how the range of qualitative, quantitative and mixed method data approaches can be most appropriately applied. It provides the knowledge and research skills you need to:
- establish the most effectual research design and method for the dissertation project and write a successful research proposal
- prepare for the project module and for a possible future research career
Project Dissertation
60 credits
This module aims to develop your ability to plan, execute and report in-depth on a major investigation.
Foundations of Machine Learning
20 credits
This module provides fundamental skills required in machine learning to solve real-world problems. These skills will help to equip the student with the fundamental principles of machine learning to support advanced topics taught in the course. Furthermore, these skills will be practical core requirements for a successful career as a machine learning engineer in industry.
Deep Learning Concepts and Techniques
20 credits
This module provides fundamental skills required in deep learning to conduct a wide variety of projects from signal processing to object detection and segmentation.
Accelerated Machine Learning
20 credits
This module provides the key skills required in accelerated machine learning to solve large scale machine learning problems. These skills will help to equip you with the fundamental principles of accelerated machine learning to support your final degree project. Furthermore, they will be practical core requirements for a successful career as a machine learning engineer in industry.
Advanced Topics in Deep Learning
20 credits
This module provides advanced skills required in deep learning to conduct a wide variety of projects in signal processing, object detection, natural language processing and time series analysis. These skills will help to equip you with advanced skills in deep learning. They are practical core requirements for a successful career as a deep learning engineer in industry.
Enterprise Machine Learning
20 credits
This module provides a best-practice set of enterprise tools for deploying large-scale machine learning projects. This will help to equip you with enterprise ready skills needed to deploy large-scale machine learning projects in industry.
Teaching
An insight into teaching on your course
Study Hours
Students should expect between nine and 12 hours of contact per week, in addition to an average of approximately 30 hours of self-study per week throughout the academic year. In the summer term, you will work solely on your project, which has an expected workload of 600 hours.
Teaching Methods
You'll gain core knowledge and understanding on this course via lectures, tutorials, practicals, coursework, projects, seminars and guided independent study. You will also receive feedback on all work you produce.
Assessment
How learning is monitored on your programme
To cater for the wide-ranging content of our courses and the varied learning preferences of our students, we offer a range of assessment methods on each programme.
Course tutors
Our staff are committed to the highest standards of teaching and learning
Dr Rubem Pereira
Programme Leader
Before joining the LJMU team, Rubem completed his PhD in Computer Network Performance Modelling in 1997. In the same year he started working at LJMU. He has been a reader in Multimedia Networks and became programme leader for MSc programmes in Computing in 2003. He has also participated in many externally funded projects, supervised PhD students and organised national and international workshops and conferences. Rubem's special areas of interest include: network technologies, distributed systems, video encoding and streaming, the future of Internet, modelling and performance analysis and evaluation of computer systems.
I really enjoy the stimulating environment associated with learning and teaching at the cutting edge of computing and ICT technology.
School facilities
What you can expect from your School
Studying at the Byrom Street site in the City Campus, which has recently enjoyed a 6 million investment, you will have access to state-of-the-art laboratories and teaching facilities. We have over 150 high performance computers including PC/ Linux Workstations and Networked Multimedia PCs for general use, in addition to the campus computing cluster. Youll also have access to an exclusive Game Technology Lab, a Computer Forensics Lab, two Multimedia Labs, a Distance Learning Lab and specialist labs for research on network security and networked appliances.
Entry requirements
You will need:
Qualification requirements
Undergraduate degree
- an undergraduate degree in a cognate subject area
or
- A Honours degree in a non-cognate subject area when supplemented by relevant skills and / or experience
or
- Degree equivalent professional qualifications, e.g. BCS Professional Graduate Diploma in IT
or
- A HND plus a minimum of three years of relevant professional experience
International requirements
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IELTS
- IELTS score of 6.0 (5.5 each component)
Further information
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Extra Requirements
- Non-standard applications are welcome. Admission will be at the discretion of the Programme Leader. Applicants may be required to submit a CV and references. Please contact the Admissions Team for further information
Application and selection
Securing your place at LJMU
To apply for this programme, you are required to complete an LJMU online application form. You will need to provide details of previous qualifications and a personal statement outlining why you wish to study this programme.
The University reserves the right to withdraw or make alterations to a course and facilities if necessary; this may be because such changes are deemed to be beneficial to students, are minor in nature and unlikely to impact negatively upon students or become necessary due to circumstances beyond the control of the University. Where this does happen, the University operates a policy of consultation, advice and support to all enrolled students affected by the proposed change to their course or module.