Course specification and structure
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PMARTINT - MSc Artificial Intelligence

Course Specification


Validation status Validated
Highest award Master of Science Level Masters
Possible interim awards Postgraduate Diploma, Postgraduate Certificate
Total credits for course 180
Awarding institution London Metropolitan University
Teaching institutions London Metropolitan University, Aventis School of Management Pte Ltd, Singapore
School School of Computing and Digital Media
Subject Area Computer Science and Applied Computing
Attendance options
Option Minimum duration Maximum duration
Full-time 1 YEARS 2 YEARS
Part-time 2 YEARS 3 YEARS
Course leader  

About the course and its strategy towards teaching and learning and towards blended learning/e-learning

The School of Computing and Digital Media is committed to provide preparation for its students which meets the current market needs, while satisfying the highest criteria for academic quality. This means teaching courses in the hottest areas of contemporary digital technologies by lecturers who are on the forefront of the technological revolution. This also means learning by having access to huge library of books, journals and reports, practicing within computer environment, which meets high standards, with hands-on experience on all aspects of the development process from problem formulation to requirements specification to software development to analysis of the solutions. And finally, this also means learning by going beyond the classes, learning by applying real research, by experimentation with cutting edge tools and technologies and by participation in real-life projects of industrial and academic significance. The MSc Artificial Intelligence course meets all these internal standards to the highest extent by combining technical resources, guaranteeing academic standards and providing additional opportunities within three research centres of the School of Computing and Digital Media – Intelligent Systems Research Centre, Cyber Security Research Centre and Centre of Communications Technology. The applied orientation of the course can also strengthen the existing relations of the School of Computing and Digital Media with the fintech industry by preparing specialists for them and by providing more competent support for research and innovation inside the industry.

The course is focused on the application of Artificial Intelligence methods to the area of complex systems, which need security protection. It provides an in-depth knowledge and practices applying the methods of Artificial Intelligence and Machine Learning to security analytics on the desktop, on the fly and on the Big Data platform on the Cloud. Such systems are based on powerful enabling technologies for developing contemporary cyber systems on the desktop, in the cyber space and on the Cloud. The course also equips the students with basic skills for constructing Internet-of-Things systems using intelligent controllers such as Arduino and Raspberry Pi, working knowledge and skills for integrating them with the Cloud and managing the data on the Big Data platform.

Appropriate blended learning technologies, such as the University’s virtual learning environment, are used to facilitate and support student learning, in particular to:

• deliver content;
• encourage active learning;
• provide formative and summative assessments with prompt feedback;
• enhance student engagement and learning.

Critical thinking is a vital constituent of any postgraduate course and lectures, seminars and tutorials provide an opportunity for students to develop this skill. Also important is the ability to arrive at alternative, practical solutions to a given problem; workshops provide an opportunity for students to develop some of these skills required by industry. Practical skills learning is an inherent part of each and every module in the course – students are given the opportunity to practice subject-specific practical skills in workshops based in suitably equipped computer laboratories. The practical AI skills using a range of developmental hardware and software platforms are developed through tutor-guided workshop exercises, sometimes using peer assistance and evaluation. Students are encouraged to exert individual effort to acquire these practical skills with guidance from tutors. Practical skills are developed progressively throughout the course and culminate (and are assessed) in the final MSc project.

Students are expected to behave throughout the course as though they are part of an AI project team and, as such, are expected to acquire and practice employability and professional skills in that context. Publicly available services by Cloud vendors such as Amazon and Microsoft will be offered for free to university students to provide technical environment in some of the modules. Team and project-based skills are emphasised throughout the course. In several modules and in the dissertation project the students might be involved in collaborative projects after DevOps methodology using public software repositories such as Gitlab. The best students will be provided the opportunity to work on educational and industrial projects directly on the private Google Kubernetes cloud of the Cyber Security Research Centre.

Course aims

The area of AI is becoming both sought after from a professional point of view and fashionable in the public perception due to the increased use of the term in media, business and industry. The UK Government1 is calling on universities to provide education, which can bring about social and economic benefit with the use of AI technology to increase efficiency and productivity. A recent Universities UK report1 highlights a talent deficit in 2030 of between 600,000 and 1.2 million workers in the financial and business sector, and technology, media and telecommunications sector. Coupled with this is that an estimated 65% of children entering primary schools will work in jobs that do not currently exist as the technological developments in AI unfold and become a reality.

According to Computer Weekly, one of the most authoritative magazines in IT industry, the demand for professionals with AI skills in the UK has almost tripled over the last three years2. The pace of growth in demand for AI roles in the UK has outstripped that in the US, Canada and Australia. In 2018, the number of AI roles advertised in the UK was 1,300 out of every million – double the rate in Canada and 20% more than in the US. Such roles are higher paid than the average UK salary, with jobs in AI advertised for an average of £56,385 a year and machine learning roles at £54,617.

However, the UK is suffering from a widespread skills gap, not only in technology in general, but in more specialised areas, such as AI. Not only are there not enough people with the technical skills required to fill AI positions, but there are also concerns over whether people currently within organisations have the skills needed for AI adoption. According to the recruitment company Indeed3, there are six times more AI roles available in Britain than there are candidates to fill them.

One of the reasons for this discrepancy is the perception that AI jobs are not for everyone, as they require highly specialised skills, so the candidates for such jobs need to be adequately prepared and trained. London Metropolitan University is committed to prove that this perception is wrong by offering the same opportunity to students from disadvantaged background and students who come from other new universities here in UK or abroad. Thus, it aims at helping people with talents to prepare themselves to work where they are needed most. The recent advances in technologies, the availability of inexpensive hardware and free software tools makes this feasible even for students without fundamental preparation in mathematics and without a strong Computing background.

There are many possible directions of specialisation of a course in Artificial Intelligence on MSc level – text and language processing, Semantic Web, robotics and embedded systems, knowledge-based systems and expert systems, automated inference, machine learning, etc. The School of Computing and Digital Media has particular interests and long-standing experience in education and research in the area of Cyber Security, which made it possible to focus on methods and applications of Artificial Intelligence in Cyber Security. This is a truly unique feature of the MSc Artificial Intelligence since the problems of Cyber Security are of extreme importance for a wide range of areas from financial industry and critical infrastructures to individual safety and privacy.


The MSc Artificial Intelligence course aims to

• Provide solid theoretical foundation for understanding the current state of art in AI research and technologies to students with traditionally technological preparation from their undergraduate study
• Focus on industrial sectors where exist wider market opportunities, like fintech industry, eCommerce, Internet service provision and software development
• Prepare the students to work in technological areas with particularly high demand for employing AI methods, such as Big Data, Cloud, Internet of Things and Cyber Security
• Achieve high degree of blending of different styles of learning by combine multiple forms of learning and teaching with particular stress on agile methodology for software development through adopting DevOps technology and utilizing public domain tools directly in the teaching process
• Strengthen the research capacity of the School for participation in REF by involving post-graduate students in research and consultancy projects of the research centres of the School
• Bring to the attention of the students social, ethical and legal problems related to AI which may affect their future professional practice (for example, privacy, security and intellectual property rights)

Course learning outcomes

The course learning outcomes have been designed to meet the requirements for certification of the British Computer Society and professional benchmarks in Computing. After completing the course the graduates are expected to

Cognitive Intellectual Skills
By the end of this programme students should be able to:

1.1 Understand the potential for applying AI technologies and for adding intelligence to existing solutions within financial and banking industry, ecommerce and online service provision for personal, business and entertainment purposes
1.2 Assess the impact of the intelligent systems on the business from economic, social, ethical and legal perspective

Subject-specific technical knowledge
By the end of this programme the students should be able to:

2.1 Understand the principles and the technologies for developing intelligent systems on different platforms – desktop, Internet, mobile, embedded and autonomous
2.2 Know the methods for securing information systems on different platforms – encryption algorithms, user profiling, data access control, etc.
2.3 Carry out critical evaluation of and comparison between a range of methods, technologies and tools for developing intelligent information systems
2.4 Have an insight into the fundamental issues related to functioning of the intelligent systems – design quality, information security, data privacy, operation safety, usability, maintainability, reusability, etc.

Subject-specific technical skills
By the end of this programme students should be able to:

3.1 Identify conceptual terms, describe domain concepts, build terminological taxonomies, specify constraints and formulate heuristics and security policies using ontological modelling languages such as RDF, OWL and SWRL
3.2 Analyse the strength and weaknesses of existing encryption algorithms and security policies for increasing the overal security of information processing
3.3 Apply Artificial Intelligence and Machine Learning models, methods and algorithms for securing complex systems

Professional practical skills
By the end of this programme students should be able to:

4.1 Produce specifications, create models and adopt standards for developing model-based and learning-driven software systems, which can collect and process large volume of data offline and online, and use suitable AI methods for their implementation
4.2 Read specifications in UML, XML and JSON, produced by others, and transform an idea for a solution of a problem into model of a system which solves this problem
4.3 Develop complex solutions, including technological prototypes, beta-test software products and experimental services that use AI methods, and apply them to the construction of Web-based, Cloud-based, mobile, embedded and autonomous applications
4.4 Prepare for attempting professional certification from Hortonworks, CompTIA Security+ and other relevant industrial frameworks for security analytics, AI, or enabling technologies

Transferable skills including those of employability and professional practice
By the end of this programme students should be able to:

5.1 Apply systematically knowledge and skills for conducting technological feasibility studies and experimental research, design complex systems and develop real-world software applications
5.2 Utilize the professional principles of project planning for progress control, time management and risk mitigation
5.3 Develop further the communication skills, which will enable team-working and co-operation with peers, tutors, and other staff.
5.4 Prepare documentation, deliver presentations and produce technical reports using text processing, graphical modelling, project management and business presentation software

Course learning outcomes / Module cross reference

Artificial Intelligence CS7050

Machine Learning CS7052

Semantic Technologies CS7051

Data Warehousing and Big Data CC7079

Cloud Computing and Internet of Things CC7080

Information Security CC7064

MSc ProjectFC7P01

Learning Outcomes covered 1-5

Principle QAA benchmark statements

The programme design has been informed by the “Subject Benchmark Statement Master’s Degrees in Computing” 2011.
http://www.qaa.ac.uk/en/Publications/Documents/SBS-Masters-degree-computing.pdf

Assessment strategy

The MSc AI course includes three groups of modules:
Methodology Modules: CS7050 Artificial Intelligence, CS7051 Semantic Technologies and CS7052 Machine Learning. These modules provide the methodological background and the specific technologies for developing intelligent applications in different areas.
Enabling Technology Modules: CC7064 Information Security, CS7079 Data Warehousing and Big Data and CS7080 Cloud Computing and Internet of Things. These modules provide the general technologies for building intelligent applications for security analytics in Cyber Security domain.
Demonstration module: FC7P01 MSc Project. This module provides the framework for demonstrating the use of Artificial Intelligence methods for solving practical problems in different areas circumscribed by the enabling technologies – security, Cloud and IoT.

The modules from these three groups will be assessed in accordance with their role in the curriculum as follows:

• All three methodological modules will be assessed by coursework for assessing the skills of using the AI methods and tools (60%) and a written exam or a coursework for evaluating the level of understanding and the decision-making skills for modelling, analysing and designing intelligent systems (40%).
• All three technological modules will be assessed by two pieces of coursework for assessing the working knowledge of technologies and the practical ability to use the supporting tools, which allows adding intelligence to the solutions in the chosen domain – business, industry or entertainment.
• The MSc project will assess the theoretical knowledge, the analytical and technical skills and decision-making potential of the students, which will guarantee their ability to add intelligent solutions to the business, industry and entertainment.

Organised work experience, work based learning, sandwich year or year abroad

• A number of enriching opportunities (paid and unpaid) are offered to students based on their performance, experience, abilities or interests. They include: research projects with teaching team, mentoring other students, support the teaching delivery of some content etc. The criteria for accessing these opportunities will be made clear to all students during the teaching year.
• Students who have professional certification and/or substantial industrial experience may be offered teaching engagements within the School of Computing and Digital Media
• Some of the students may also be offered to serve as mentors of the undergraduate students in relevant fields

Course specific regulations

N/A

Modules required for interim awards

• To obtain the degree award of MSc Artificial Intelligence the students need to complete successfully all 6 taught modules and the MSc dissertation project module (FC7P01 MSc Project).
• Completing all six taught module included in the course curriculum entitles the students to the PGDip Artificial Intelligence award.
• Completing the three CS modules from the curriculum – CS7050 Artificial Intelligence, CS7052 Machine Learning and CS7051 Semantic Technologies – entitles the students to the PGCert Artificial Intelligence award.

Arrangements for promoting reflective learning and personal development

The course includes two semesters of formal scheduled teaching for full-time study (and up to four semesters for part-time study) where students will learn their knowledge and skills for developing intelligent applications, followed by one semesters dissertation project for full-time mode of study (and one or two semesters for part-time mode) where they will apply the knowledge and skills learned to exercise intelligent solution development. During their study the students are encouraged to reflect on their learning by various means:

• In all modules the students are encouraged to write blogs/log books to reflect on what they have learned each week, and to maintain a personal development portfolio;
• In some of the modules the work on practical workshop tasks is assessed and contributes to the final module mark for increasing the engagement in the students in the practical work;
• Formative feedback is provided during the semester in each module so that students are able to discuss draft coursework in tutorials and workshops in order to refine and enhance their work before final submission.
• It is expected that after the formal scheduled teaching element students will be able to propose project topics which better place them within the professional context as well as better utilize the knowledge and skills gained while studying.
• Students will also be able to reflect upon their learning thereby further enhancing their employability and career prospects by participating in additional free seminars organised by the learning technologies department of the University. This time will be used to evaluate the different theories, models, technologies and tools learned.

Career, employability and opportunities for continuing professional development

On completion of the course graduates will be well equipped to work in both academic and business environments on AI-related problems. They will understand the principles of the terminological vocabularies, will be able to model various domains on an abstract logical level and will be familiar with the technologies for building intelligent solutions in these areas. Based on this professional preparation, the graduates will be eligible for research-intensive and highly adaptive jobs above junior level, such as research fellows, system analysts and solution architects, as well as members of development teams using the methods of artificial intelligence and machine learning. The course also provides an excellent basis for further study for those wishing to pursue a higher-level research degree, e.g. PhD, or embark on an industry-based career as independent consultants.

The course includes two technological modules, which prepare for professional certification from Oracle (Database and Data Warehousing platforms), Hortonworks (BigData platform), Amazon (Cloud platform). More information concerning this can be found on the corresponding corporate sites.

Professional Statutory and Regulatory Body (PSRB) accreditations & exemptions

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.

Career opportunities

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.

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.

To study a degree at London Met, you must be able to demonstrate proficiency in the English language. If you require a Tier 4 student visa you may need to provide the results of a Secure English Language Test (SELT) such as Academic IELTS. For more information about English qualifications please see our English language 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.

Official use and codes

Approved to run from 2019/20 Specification version 1 Specification status Validated
Original validation date 06 Aug 2019 Last validation date 06 Aug 2019  
Sources of funding HE FUNDING COUNCIL FOR ENGLAND
JACS codes
Route code ARTINT

Course Structure

Stage 1 Level 07 September start Offered

Code Module title Info Type Credits Location Period Day Time
CS7050 Artificial Intelligence Core 20 NORTH AUT MON PM
CS7051 Semantic Technologies Core 20 NORTH SPR MON EV
CS7052 Machine Learning Core 20 NORTH AUT WED AM
CS7064 Information Security Core 20 NORTH SPR THU AM
CS7079 Data Warehousing and Big Data Core 20 NORTH AUT THU AM
CS7080 Cloud Computing and the Internet of Things Core 20 NORTH SPR THU PM
FC7P01 MSc Project Core 60 NORTH SPR WED PM
          NORTH AUT WED PM
          NORTH SUM WED PM

Stage 1 Level 07 January start Offered

Code Module title Info Type Credits Location Period Day Time
CS7050 Artificial Intelligence Core 20        
CS7051 Semantic Technologies Core 20 NORTH SPR MON EV
CS7052 Machine Learning Core 20        
CS7064 Information Security Core 20 NORTH SPR THU AM
CS7079 Data Warehousing and Big Data Core 20        
CS7080 Cloud Computing and the Internet of Things Core 20 NORTH SPR THU PM
FC7P01 MSc Project Core 60 NORTH SUM WED PM
          NORTH SPR WED PM