Why study this course?

Our Artificial Intelligence MSc degree is the ideal choice if you want to progress or start your career in the computer science, data or software engineering industry. The course is designed for those who want to play a part in transforming the relationship between people and technology, as well as those who want to be on the forefront of the current digital revolution that is spanning all sectors – from science and engineering to business and entertainment.

Artificial Intelligence (AI) is no longer confined to academic research, it’s a growing area of computer science that is beginning to be utilised across all industries that want to gain more from their technology by adding intelligence and learning capabilities to their products.

Recent advances in knowledge representation, pattern recognition, natural language processing and machine learning have made it possible to construct software that solves problems beyond human reach. As a result the industry is experiencing a huge demand for AI specialists. On graduation you’ll have a wider pool of opportunities to choose from, a clear career path and a higher starting salary than average across all professions.

Our Artificial Intelligence MSc degree has been accredited with partial CITP status by BCS, The Chartered Institute for IT. This accreditation is a mark of assurance that the degree meets the standards set by BCS. As a graduate of this course, accreditation will also entitle you to professional membership of BCS, which is an important part of the criteria for achieving Chartered IT Professional (CITP) status through the Institute.

The course has also achieved partial CENG status by BCS on behalf of the Engineering Council. Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng).

Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

More about this course

Our Artificial Intelligence MSc curriculum is well-balanced as it combines two groups of modules – fundamental modules and specialist modules.

On this course you’ll learn how to develop AI software and apply AI methods through:

  • collecting, aggregating, filtering and transporting data from files, databases and live data sources in real-time
  • modelling, extracting, correlating and analysing data from memory and repositories in automatic, interactive and batch modes of operation
  • designing, developing and testing intelligent programmes, which can perform automated reasoning and learning
  • constructing and controlling autonomous devices, such as robots and drones

A distinctive feature of our Artificial Intelligence MSc is the programme’s focus on various aspects of security, including information, networks and cyber security. You’ll learn how to conduct security analytics in an intelligent way, which is a growing AI field in the detection, identification, classification, prediction and prevention of threats.

You’ll finish your degree with a dissertation, which you’ll have the opportunity to link with the work of one of our two research centres – the Cyber Security Research Centre or the Intelligent Systems Research Centre. On the course you’ll also have the opportunity to work on collaborative projects with our long-standing partners in the fintech industry, including Lloyds Banking Group, Callsign and Open Banking Inc.

Although a master’s degree in artificial intelligence does not lead to a professional qualification on its own, you’ll find that some of the modules provide preparation for professional qualifications in areas such as big data management, data analytics and cyber security.


All three artificial intelligence fundamental modules will be assessed by combining coursework (60%) and exams (40%). Coursework will assess your skills in using AI methods and tools, while exams will evaluate your level of understanding and decision-making skills in analysing and designing intelligent systems.

The specialist modules will be assessed by two pieces of coursework, to assess your knowledge of technologies (50%) and practical ability to use tools (50%). The skills you’ll develop through these assessments will allow you to add intelligence to solutions in your chosen domain – business, industry or entertainment.

The dissertation project will assess your theoretical knowledge, analytical and technological skills, as well as your decision-making potential.

Professional accreditation

Although AI does not lead to a professional qualification on its own, some of the modules included in the course curriculum provide preparation for professional qualification in related areas, such as big data management, data analytics and cyber security.

Fees and key information

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Entry requirements

You will be required to have:

  • a 2:2 UK degree (or equivalent) in computer or data science, computer, software or network engineering, computing or ICT, cyber security

You will also be considered if you have:

  • a 2:2 UK degree (or equivalent) that requires mathematics and computing skills, including mathematics, physics, chemistry, economics, business or finance

Applicants with relevant professional experience will also be considered.

Programming skills with one of the popular languages such as Java or Python would also be a great advantage.

Accreditation of Prior Learning

Any university-level qualifications or relevant experience you gain prior to starting university could count towards your course at London Met. Find out more about applying for Accreditation of Prior Learning (APL).

English language requirements

To study a degree at London Met, you must be able to demonstrate proficiency in the English language. If you require a Student visa you may need to provide the results of a Secure English Language Test (SELT) such as Academic IELTS. This course requires you to meet our standard requirements.

If you need (or wish) to improve your English before starting your degree, the University offers a Pre-sessional Academic English course to help you build your confidence and reach the level of English you require.

Modular structure

The modules listed below are for the academic year 2023/24 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:

This module currently runs:
  • autumn semester - Monday morning

This module introduces the essential principles, methods and techniques in AI. It covers a broad range of topics such as search, planning, logic, knowledge representation and machine learning. It discusses examples of intelligent systems and studies how to develop intelligent applications such as expert systems, learning systems, and autonomous mobile and robotic systems. Students will be offered lectures, which introduces the important concepts, explain the principles and techniques, and demonstrate how to apply them to solve problems in the related topics. The workshops will provide practical sessions to help students understand the content of the lectures and build the necessary skills to develop intelligent systems.

This module currently runs:
  • spring semester - Thursday afternoon

This module provides students with an in-depth appreciation of the Internet of Things (IoT) and Cloud Computing concepts, models, infrastructures and capabilities. The module will place emphasis on modern system architecture and design, Autonomous Intelligent Systems (AIS), key wireless/mobile/sensor technologies, and issues of privacy and trust, in the development of Cloud-based IoT systems. Practical work within the module will provide students with real, hands-on, experience of building a basic Internet of Things infrastructure that can access Cloud Computing services and the opportunity to develop their Python programming skills and abilities. Some basic knowledge of Python will be used throughout. Understanding of various Intelligent, wired and wireless technologies could be an advantage.

This module currently runs:
  • autumn semester - Thursday morning

The module aims to strengthen students’ skills in data technologies ranging from database and data warehousing to Big Data. First, it will provide students with good understanding of database concepts and database management systems in reference to modern enterprise-level database development. Once gaining good skills in database development, students will be able to study and gain an in-depth understanding of data warehousing which include concepts and analytical foundations as well as data warehousing development. Through intensive hands-on sessions, the students will be able to get familiar with related technological trends and development in the field. the module will leverage a portfolio of SQL server tools that include SQL Server DBMS, SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS) and SQL Server Analysis Service (SSAS) to provide hands-on experience in implementing a reporting solution through a combination of assignments and lab exercises.
The module introduces also the foundation of Big data management based on Apache Hadoop platform and provides students with a broad introduction to Big Data technologies. This will involve hands-on sessions, designed for data analysts, business intelligence specialists, developers, administrators or anyone who has a desire to learn how to process and manage massive and complex data to infer knowledge from data. Topics include Hadoop, HDFS, MapReduce, Spark, Sqoop, Hive, Pig and MLlib.

This module currently runs:
  • summer studies - Wednesday afternoon
  • spring semester - Wednesday afternoon
  • autumn semester - Wednesday afternoon

The module provides students with the experience of planning and bringing to fruition a major piece of individual work. Also, the module aims to encourage and reward individual inventiveness and application of effort through working on research or company/local government projects. The project is an exercise that may take a variety of forms depending on the nature of the project and the subject area. Particular students will be encouraged to carry out their projects for local companies or government departments.
Semester: Autumn/Spring/Summer
Prerequisites: all course specific core modules
Assessment: 100% coursework (project viva is compulsory for all students)

Prior knowledge: Understanding of research management, planning and LSEP issues

The module aims to encourage and reward individual inventiveness and application of effort. It also aims to allow students:
- To have an opportunity to assimilate the knowledge they gained in their course and extend this knowledge to new area of application.
- To apply newly acquired knowledge and techniques to a specific problem using established research techniques and methods.
- To determine the framework of the project according to a set of specifications relevant to the subject of study.
- To manage an extended piece of work by confining the problem within the constraints of time and available resources.
- To research effectively the background material on the topic using a variety of sources and to develop ability to conduct critical analysis and draw conclusions.
- To develop the ability to produce detailed specifications and design frameworks relevant to the problem of investigation in the subject related to the industry.
- To demonstrate the originality in the application of new knowledge and skills.
- To effectively communicate the work to others by means of verbal and appropriate documentation techniques.
- To raise awareness in potential business development opportunities in an area pertinent to the topic.

This module currently runs:
  • autumn semester - Wednesday morning

This module provides a comprehensive overview of the methods for machine learning and data analytics suitable for use in the data analysis and Big Data Analytics. It also provides practical skills for working with various tools for data analysis and Big Data Analytics inside and outside the platform such as Python, R, Spark, etc. The knowledge and skills obtained can be used in many tasks where the data analysis is of crucial importance for the competitiveness and the effectiveness of the businesses – customer profiling, product recommendations, market trends analysis, cybersecurity, etc. Some basic programming skills using languages for numerical processing such as Python or R or other relevant languages can be an advantage.

This module currently runs:
  • spring semester - Monday afternoon

The module is designed to impart essential mathematical principles and concepts of computer vision alongside its practical applications. The module encompasses core topics in image formation and low-level image processing; mid-level scene representation; model-based description and tracking. Appropriate hardware/software tools will be integrated into the module to enable students to apply and test computer vision algorithms on real world data sets.

The objectives of this module are to:
• enable students to gain understanding of essential concepts of computer vision algorithms and systems
• develop students' expertise in analysing, designing, and testing vision algorithms and systems
• train students in using appropriate hardware/software tools for solving common computer vision problems
• prepare students with postgraduate level research and report writing skills

This module currently runs:
  • spring semester - Thursday morning

The module is concerned with the study and application of tools and techniques that enable the protection of information and other resources of enterprise information systems. Increases in storage, manipulation, and transfer of data across computer networks requires effective encryption techniques. This module will provide insight into some of those techniques, algorithms and their development through history. Part of the course is dedicated to the mathematics (number theory, finite fields and elliptic curves) relevant to cryptography with techniques developed using software such as Maple. The focus will also be on the analysis, design and implementation of tools and techniques that achieve the three goals of confidentiality, integrity and authenticity in security computing. Particular focus will be on the management framework that facilitate the accomplishment of the above three goals. Importantly the module will address the ethical framework of information security, the issues around privacy and data protection and the rights of private citizens to access strong encryption. Throughout the module connections with other aspects of artificial intelligence and cybersecurity will be emphasised through the examples and case studies chosen.

This module currently runs:
  • summer studies - Monday morning
  • summer studies - Wednesday afternoon
  • spring semester - Thursday afternoon

This module develops students’ foundation of programming principles through the introduction of application programming for data analytics. The module covers common programming data structures, flow controls, data input and output, and error handling. In particular, the module places emphasis on data manipulation and presentation for data analysis. A substantial practical element is integrated into the module to enable students to use a programming language (e.g. Python) to prepare data for analysis and develop data analytical applications.

The aims of this module are to:
• enable students to gain understanding of programming principles,
• develop students’ knowledge and skills in programming design and coding,
• develop students expertise in data manipulation and presentation for data analysis,
• develop students with practical skills in data analytical applications development, and
• enhance students skills for integrative reasoning, problem-solving and critical thinking.

This module currently runs:
  • spring semester - Monday morning

This module provides the theoretical foundations, the technologies and the corresponding tools for constructing intelligent model-driven software systems with explicit representation of knowledge. It will enable the students to model, design and implement software systems, which demonstrate “artificial intelligence” similar to the intelligence of the human behaviour. At the same time, it will help the students understand better the rationality behind the human intelligence.

The module follows one of the two main methodologies for dealing with the phenomena of Artificial Intelligence in computer science, known as “semiotic”, “symbolic” or “logical” paradigm. In this approach, the intelligent behaviour is achieved by incorporating common sense, heuristic and expert rules of behaviour which control the programmed algorithms for information processing during their execution in real time. For this purpose the module introduces a number of formal languages, such as FOL, DL and HCL, used for modelling the rational behaviour by logical methods, the corresponding mark-up languages, such as RDF, OWL and SWRL, which provide the necessary technology for representing the logical models in XML format alongside the respective software tools.

The module relies on some basic knowledge of discrete mathematics and formal logics typically taught in most of the undergraduate courses in engineering and science. Although it does not require extensive programming experience beyond the first introductory course in programing, working skills in programming using general-purpose programming languages such as Java can be greatly beneficial.

After completing this module, the students can enhance further their skills by studying the methods for automated deduction, semantic disambiguation and language translation.

This module currently runs:
  • summer studies - Tuesday morning
  • spring semester - Wednesday afternoon
  • summer studies - Monday afternoon

This module will introduce students to modern statistical modelling techniques and how those techniques can be used for prediction and forecasting. Throughout the statistical environment and software R will be used in conjunction with relevant statistical libraries.
The module will, introduce modern regression techniques (including smoothing), discuss different model selection techniques (including the classical statistical hypothesis) and how those techniques can be used for prediction purpose.

Prior learning: Statistical knowledge desirable.

The module aims to:

1. Equip graduate students with modern statistical techniques
2. Provide students with some selected advanced statistics topics including forecasting
3. Prepare students to be able to read and understand professional articles
4. Prepare students to carry on their own research and use modern statistical techniques as one of the tools for their research.

Where this course can take you

Our Artificial Intelligence MSc provides a solid foundation for further qualifications in professional areas that require knowledge of applied mathematics, as well as further study at PhD level. The study of AI is both challenging and hugely rewarding. Learning about the process of extracting, acquiring and growing our knowledge has a great impact on our understanding of how human intelligence works.

AI also applies to many areas of the computer science industry where most extensive data processing, electronic communication or software development takes place. As a result, this MSc qualification will provide you with increased career opportunities than those of traditional computer and data scientists, or computer, software and network engineers. Knowledge of AI solutions will enhance your job prospects with businesses such as online retailers, fintech companies, corporate enterprises, technology start-ups, electronic entertainment vendors and network service providers.

Additional costs

Please note, in addition to the tuition fee there may be additional costs for things like equipment, materials, printing, textbooks, trips or professional body fees.

Additionally, there may be other activities that are not formally part of your course and not required to complete your course, but which you may find helpful (for example, optional field trips). The costs of these are additional to your tuition fee and the fees set out above and will be notified when the activity is being arranged.

How to apply

Use the apply button to begin your application.

If you require a Student visa and wish to study a postgraduate course on a part-time basis, please read our how to apply information for international students to ensure you have all the details you need about the application process.

When to apply

You are advised to apply as early as possible as applications will only be considered if there are places available on the course.

To find out when teaching for this degree will begin, as well as welcome week and any induction activities, view our academic term dates.

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