University of Malta

Overview Description
UOM Main Page
Apply - Admissions 2016
Campus Map button

COURSE TITLE Master of Science in Artificial Intelligence [Taught and Research (Mainly by Research)]








Second Cycle

Level 7

5 Semesters

Part-time (by Research)


This programme of study is also offered on a full-time basis. Please consult the Registrar’s website for more information pertaining to courses offered by the University.

The M.Sc. in Artificial Intelligence (AI) is a degree course in one specific branch of ICT that deals with simulating intelligence within real-world applications. This degree course has been purposely designed for working professionals, as well as, graduates who have just completed their undergraduate degree to form a strong and deep understanding of AI topics and eventually either converge into a possible proposed thematic area (Big Data, Creative Technologies or Artificial Vision) or simply a generic advanced AI domain, before proceeding to a thesis. The programme has been purposely flexibly designed as to available for those who would like to intensely work to complete it full-time within a calendar year, or part-time over 2 years, while accommodating those candidates who have a day job. Finally, this Masters is intended to appeal and address current and future industry demands and niches by allowing the possibility of internships with industry partners as part of applied projects and/or the final project.

For Further details please click here
LEARNING OUTCOMES The core first part of the M.Sc. in AI ensures that students will:

1. Be able apply their previous knowledge from their undergraduate degree to applied intelligent computer systems;

2. Relate statistical and data theories and applications to basic intelligent systems;

3. Gain insightful and practical knowledge that will later apply to specific focuses like big data analytics, creative technologies and artificial vision;

4. Acquire basic skills that will help them through the second part of the course but also to employ them in everyday life involving big data, creative technologies and artificial vision.

In the second part of the course the students can focus on three distinct research domains that each carries a specific programme rational and learning outcomes.

The Big Data stream focuses on a strong theoretical background in machine learning, statistics, and data mining with advanced knowledge of computational and statistical data analysis. An advanced knowledge and appreciation of non-statistical approaches to data and distributed systems and large scale databases also forms an integral part of this stream. Finally, an appreciation of how the role of a data analyst or scientist fits into the organisational and development processes of a company is covered.

The learning outcomes for the big data stream are:

1. A highly analytical approach to problem solving;

2. Ability to extract value and insight from data;

3. Ability to analyse and critically evaluate applicability of both machine
learning, statistical and data mining approaches;

4. Ability to work with big amounts of structured and unstructured data;

The Creative Technologies stream focuses on smart technologies that are becoming increasingly important for the creative industries. The skills associated with the once-separate creative and technical worlds are beginning to overlap more and more, especially with the rise of smart interfaces and wearable devices. The scope of this Masters focus stream is to serve as a link between these two worlds thus creating professionals capable of bridging the gap which exists between the two.

Students will:

1. Be prepared for a career as technology-led experts in the creative industries;

2. Learn how to design, develop and apply software in various areas of the creative industries;

3. Be aware of the fundamental concepts behind intelligent computing;

4. Have a clear sense of the issues involved in building and maintaining reliable software for the sophisticated demands of today’s market;

5. Understand the social context and visual design aspects of software development.

The Artificial Vision stream focuses on the state –of-the-art techniques that extract information from images and videos. Artificial vision is proving to be crucial in various industrial applications, such as manufacturing (eg. Visual quality inspection), entertainment (eg. Capture body movement with Kinect sensor), robotics (eg. Exploring a new place), health (eg. Medical image processing), and security (eg. Pedestrian and car tracking), among others. The nned for further development of artificial vision is increasing exponentially and so are the career opportunities.

Students will:

1. Gain broad knowledge on various state –of the-art algorithms

2. Understand how the visual system of the brain processes visual information

3. Understand the challenges of artificial vision algorithms in real-world applications

4. Develop hands on experience in the implementation of various algorithms using Matlab/Python

5. Develop the ability to analyse and critically evaluate applicability of artificial vision algorithms for given problems

6. Be prepared for a career in the vision-based industries

7. Have the opportunity of a research internship with another European University.
CAREER OPPORTUNITIES AND ACCESS TO FURTHER STUDY On successfully completing the M.Sc. in AI the postgraduates can further pursue their studies towards a Ph.D. or apply their acquired knowledge to their line of work by fruitfully applying either creative technologies, big data analytics or artificial vision to their line of work. Alternatively, new possibilities to switch to a fresh career within these three areas is possible as current industry needs are indicating a need and a strong potential to employ professionals with strong understanding and expertise within the three streams being offered.
COURSE INTENDED FOR The M.Sc. in AI appeals to all graduates from a degree which has a strong ICT component, but the board of studies will also take into consideration candidates with a strong experience track record evidenced by a substantial portfolio of work in industry.
The Course shall be open to applicants in possession of one of the following qualifications:

(a) the degree of Bachelor of Science in Information Technology (Honours) - B.Sc. I.T. (Hons) - with at least Second Class Honours or

(b) the degree of Bachelor of Science (Honours) in Information and Communication Technology - B.Sc. (Hons) I.C.T. - with at least Second Class Honours or

(c) the degree of Bachelor of Science (Honours) in Computing Science or in Computer Engineering - B.Sc. (Hons) - with at least Second Class Honours or

(d) the degree of Bachelor of Engineering (Honours) - B.Eng.(Hons) - with at least Second Class Honours in an ICT related area of study or

(e) any other Honours degree with a strong ICT component which the Board deems comparable to the qualifications indicated in (a), (b), (c) or (d) or

(f) a Third Class Honours degree in an ICT related area of study together with a professional qualification/s or experience as evidenced by a substantial portfolio of recent works, deemed by the University Admissions Board, on the recommendation of the Faculty Admissions Committee, to satisfy in full the admission requirements of the Course.

The admission of applicants under paragraph (f) may be made conditional on the results of an interview conducted for the purpose.

Interviews, when necessary, shall be conducted by a board composed of at least three members.

The admission requirements are applicable for courses commencing in October 2016.

For more detailed information pertaining to admission and progression requirements please refer to the bye-laws for the course available here.



Last Updated: 5 April 2017
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
Unless for exceptional approved reasons, no changes to the programme of study for a particular academic year will be made once the students' registration period for that academic year begins.

For applicable fees please check the link on the Finance Office webpage.
Study-unit Registration Forms 2017/8


For Undergraduate (Day) and Postgraduate students.


Academic Advisors 2017/8


Academic Advisors for ICT 1st year students (Intake 2017/8), NOW available

Faculty of ICT Timetables


ICT Timetables are available from Here.

Health and Safety Regulations for Labs Form

The Faculty of ICT Health and Safety Regulations for Laboratories form can be found here



Log In back to UoM Homepage