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 Master of Science in Signal Processing and Machine Learning aims to instil a high-level knowledge in the areas of Signal Processing and Machine Learning with additional focus on fostering research and development of new ideas in these areas.
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, provided that for the degree in Signal Processing and Machine Learning, other areas of study deemed relevant by the Board may also be considered 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 2020.
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.
After you receive an offer from us, our International Office will assist you with visas, accommodation and other related issues.
Annual Enrolement Fee: Eur 400
Total Tuition Fees: Eur 13,400 Yr 1: Eur 6,700 - Yr 2: Eur 6,700
The M.Sc. in Signal Processing and Machine Learning seeks to give a solid understanding of the theory, practice and the current research status in signal processing and machine learning tools, and their application in specific domains. Through a selection of core and advanced elective topics students will be able to select the areas of greatest interest leading to the M. Sc. Dissertation.
Overall learning outcomes include:
• an understanding of the mathematical basis and engineering concepts, as well as a comparison of techniques currently in use. • ability to select and combine a subset of the techniques learnt to hypothesise a solution to a specific real-world or synthesised problem. • familiarity with software and hardware tools that are currently available for the development of signal processing and machine learning solutions. • an appreciation of research methods necessary to publish in these areas, enabling students to follow research as a professional job.
This course is a second cycle degree programme intended for graduates having a science or engineering background and are interested in: advancing the science of signal processing and machine learning, applying signal processing and machine learning techniques to every day real-world problems, and for practitioners working in domains where signal processing and pattern recognition are key to the technologies being developed or used.
M.Sc. in Signal Processing and Machine Learning find employment in various sectors of the industry, including but not limited to: manufacturing, finance, game development, embedded systems, and software development. Furthermore, the knowledge and skills attained during this course open doors for graduates to seek employment with large research groups in industry, research institutes, and universities abroad.
This degree enables also further studies leading to a doctorate degree.
Click here to access the Programme of Study applicable from 2020/1.
Last Updated: 30 September 2020
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.