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

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

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Course information

Course title Master of Science in Artificial Intelligence [Taught and Research (Mainly by Research)]
Course code PMSCIARIPET7
Postnominal M.Sc.(Melit.)
Level of qualification Second Cycle
National Qualifications Framework level Level 7
Duration 4 Semesters
Mode of attendance Part-time Evening (mainly by Research)
Total ECTS credits 90
Coordinator Matthew Montebello
Delivered by Faculty of Information and Communication Technology
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, Computer Vision, Robotics, Fintech or Geosciences) or simply a generic advanced AI domain, before proceeding to a dissertation. 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.

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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 a suitable 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 or

(g) any other Honours degree obtained with at least Second Class Honours, provided that applicants would have successfully completed at least three individual study-units as directed by the Board, prior to being admitted to 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.

Eligible applicants in terms of paragraphs (a) to (f) may register as visiting students for individual study-units, as directed by the Board, and obtain credit for them. Should applicants be accepted to join the Course within 5 years from following the first study-unit, the Board may allow the transfer of credits to the student’s academic record for the Course in lieu of comparable units in the current programme for the Degree.

The admission requirements are applicable for courses commencing in October 2021.

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

UM currently hosts over 1,000 full-time international students and over 450 visiting students. The ever-increasing international students coming from various countries, in recent years, have transformed this 400-year old institution into an international campus.

Our international students generally describe Malta as a safe place, enjoying excellent weather and an all-year varied cultural programme. Malta is considered as the ideal place for students to study.

You can compare your national qualifications to the local requirements by visiting our qualifications comparability webpage.

Access more information about our admission process and English language requirements.

After you receive an offer from us, our International Office will assist you with visas, accommodation and other related issues.

Local/EU/EEA Applicants: Total Tuition Fees: Eur 5,700
Fee per semester: Eur 1,425

Bench fees may apply - Kindly refer to 'Bench Fees Indicative Charges' document available at:

Non-EU/Non-EEA Applicants: Total Tuition Fees: Eur 13,400
Yr 1: Eur 6,700 - Yr 2: Eur 6,700

Bench fees may apply - Kindly refer to 'Bench Fees Indicative Charges' document available at:
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, Computer Vision, Robotics and Fintech;

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, Computer Vision, Robotics and Fintech.

In the second part of the course the students can focus on four 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 Computer Vision stream focuses on the state–of-the-art techniques that extract information from images and videos. Computer Vision is proving to be crucial in various industrial applications, such as manufacturing (eg. Visual quality inspection), entertainment (eg. Capture body movement), robotics (eg. Exploring a new place), health (eg. Medical image processing), and security (eg. Pedestrian and car tracking), among others. The need for further development of Computer 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 Computer Vision algorithms in real-world applications;

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

5. Develop the ability to analyse and critically evaluate applicability of Computer 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.

The Robotics stream offers different facets of Artificial Intelligence, bringing together aspects of Robotics, Natural Language Processing and Artificial Vision. Automation is an ever growing industry in AI and, through this stream, you will receive applied, hands-on training in preparation to meet both industry demands, as well as gain the necessary knowledge to undertake research in this exciting field. Robot/Machine-Human interaction is becoming ubiquitous in our everyday devices.

Learning outcomes from the Robotics stream are:

1. Understanding of embedded and control systems;

2. Understand the technologies used by a machine to understand, process and generate language;

3. Understand the technologies used in vision processing and how images can be classified;

4. Be able to implement solutions to different AI problems.

The Fintech stream will provide students with the core elements of financial technologies and will educate them on how to digitally transform business operations using AI techniques. In addition, students will obtain deeper knowledge of financial systems, artificial intelligence, and big data analytics. The use of emerging technologies like Blockchain and Cryptocurrencies will also be covered. Fintech is quickly driving efficiency up and costs down through to the digitalisation of transactions thus creating a cross-disciplinary science. The course will equip students with the essential skills and knowledge for a career in this field; it combines theory, intensive practice and industrial engagement. The initiative is also expected to contribute significantly to the digital transformation of the country’s financial services industry especially since Malta is aiming to become a Fintech hub in the coming years.

The learning outcomes for the Fintech stream are:

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

2. Understand the challenges of Computational Finance in real-world applications;

3. Develop a highly analytical approach to problem-solving;

4. Learn to extract value and insight from data;

5. Seek to analyse and critically evaluate applicability of both machine learning, statistical and data mining approaches within the context of Computational Finance;

6. Develop hands-on experience in the implementation of various Computational Finance algorithms;

7. Develop the ability to analyse and critically evaluate applicability of Computational Finance algorithms for given problems;

8. Be prepared for a career in the Computational Finance industries.

The Geosciences stream focuses on the use of machine learning, data mining, and statistical data analysis on geosciences generated data. During the last decade, advancements in computation hardware, computer modelling, and instrumentation, has resulted in a deluge of data. Manual evaluation of this volume of information is impractical, if not impossible. The Geosciences stream combines the understanding of the fundamental principles of earth processes and artificial intelligence techniques with the aim of providing a physical understanding to the information extracted from data analysis. Cutting edge and state-of-the-art methodologies for the processing of big data coming from atmospheric, oceanographic, and solid earth phenomena form an integral part of this stream.

The learning outcomes for the Geosciences stream are:

1. Describe the basic fundamental principles of earth processes;

2. Describe how geosciences data can be generated;

3. List recent advances in the application of artificial intelligence techniques to geosciences data;

4. Process satellite data to generate important earth parameters;

5. Apply big data methodologies to geosciences generated data.
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.
On successfully completing the M.Sc. in AI the graduates can further pursue their studies towards a Ph.D. or apply their acquired knowledge to their line of work by fruitfully applying either big data analytics, computer vision, robotics or computational finance to their line of work. Alternatively, new possibilities to switch to a fresh career within these four 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 streams being offered.
Click here to access the Programme of Study applicable from 2021/2.

Last Updated: 29 March 2021

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.