Master's Programme (120 credits) in Information Technology - Data Science, AI and Machine Learning, 120 credits

Degree of Bachelor of Science in Engineering or Degree of Bachelor of Science in an engineering subject. 90 credits in computer engineering, computer science or electrical engineering including 15 credits programming in generic languages (e.g., Python/C/C++/Java/Matlab or similar) and thesis 15 credits. 30 credits mathematics including calculus, linear algebra and transform methods. The degree must be equivalent to a Swedish kandidatexamen or Swedish högskoleingenjörsexamen and must have been awarded from an internationally recognised university.

Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School.

The primary objectives of the study programme is that the student shall develop theoretical and practical competence for research, development and practical construction within the main area of computer science and engineering. Fundamental in the study programme is an industrial perspective on information technology.

The study programme also has the objective that the student shall gain deepened knowledge within some of the following more specific subject areas: machine learning, big data parallel computing, image analysis, data mining and digital service innovation.

The student shall with the programme reach a sufficient basis for PhD studies or advanced project work within industry.

This Master''s degree prepares the student for doctoral studies in the field of information technology.

Upon completion of the programme the student shall be able to:

Knowledge and understanding

• describe products and systems within the area of data science
• describe the methods that are applied when developing new system solutions and their importance and use in the field of technology
• discuss the level of international research and its contemporary development, especially in regard to the area of data science

Skills and ability

• search for solutions to technically complex research tasks and plan and implement a solution to an advanced technical development task within given time frames
• use system oriented analytical methods and tools within computer science and engineering
• systematically compare own work to international research in the area
• implement an advanced technical development task within given time frames
• present and defend own work, orally as well as in writing, in an international research environment

Judgment and approach

• assess and evaluate work in research and development, based on own experience, from technical as well as social and ethical aspects
• make judgements about Information technology with respect to opportunities, limitations, and its role in society, as well as the responsibility of computer engineers for how it is applied
• identify own needs for additional knowledge and independently take responsibility for own knowledge development.

In year 1 fundamental courses within the main area of the programme are taken. These courses are mainly introductory and overview relevant methods and theories within the area of data science, including also societal and ethical aspects. Further the student takes courses for deeper knowledge in machine learning and image analysis, but also shall gain understanding of the possibilities and limitations of distributed computations and programming of parallel platforms.

In year 2 there are courses where students can develop a higher level perspective on extracting knowledge from data and to use this knowledge for developing new digital services. The student also completes a master´s thesis and has the option to choose an advanced-level elective course (7.5 credits).

Instruction is generally in the form of lectures, seminars, laboratory work, consultation and project work. Several courses have compulsory assignments that shall be presented both in writing and orally.

Teaching language is English.

The students learn about key topics such as equality/diversity, sustainable development, and collaboration in courses such as Perspectives on Data Science and the thesis, where the students are required to discuss social, environmental, and economical considerations. Furthermore, there is an emphasis on sustainable learning activities like project-based learning, and the connection to working life is further supported by various chances to meet industry representatives (e.g., in guest lectures, study visits, and events). This programme is an international programme with most students coming from abroad, all courses in English, various courses for exchange students, and a highly international group of teachers.

The following courses are offered within the programme

(2nd - Second cycle)

Courses marked with asterisk (*) are included in the programme main area Computer Science and Engineering.

Year 1

Python - a Gateway to Machine Learning, 7.5 credits (2nd)*

Data Science: Theory, Practice and Societal Implications, 7.5 credits (2nd)*

Artificial Intelligence, 7.5 credits (2nd)*

Engineering Mathematics, 7.5 credits (2nd)

Learning Systems, 7.5 credits (2nd)*

Edge Computing and Internet of Things, 7.5 credits (2nd)*

Image Analysis, 7.5 credits (2nd)*

Big Data Parallel Programming, 7.5 credits (2nd)*

Year 2

Data Mining, 7.5 credits (2nd)*

Digital Service Innovation, 7.5 credits (2nd)*

Deep Learning, 7.5 credits (2nd)*

Thesis, 30 credits (2nd)*

Elective courses:

Computer Vision in 3D, 7.5 credits (2nd)*

Artificial Intelligence for Health, 7.5 credits (2nd)*

There is an opportunity to replace the elective course in year 2 with an optional advanced-level course (7.5 credits) within the areas of engineering, mathematics, innovation and entrepreneurship.

The University reserves the right to cancel courses chosen by less than 12 students.

Degree of Master of Science (120 credits) with a major in Computer Science and Engineering (Teknologie masterexamen med huvudområdet datateknik).

To be eligible for semester 2, a minimum of 7.5 credits from semester 1 is required.

To be eligible for semester 3, a minimum of 40 credits from semesters 1-2 is required.

To be eligible for semester 4, a minimum of 60 credits from semesters 1-3 is required.

The programme is continuously monitored and evaluated through course evaluations conducted after the completion of each course. Course evaluations serve as a tool to, based on students' experiences and feedback, make changes to the content and delivery of courses. Upon completing their studies, all students are offered the opportunity to participate in a programme evaluation by completing a final-year survey. The results of this survey can lead to changes in the programme. The programme is associated with a programme council that addresses quality and development issues. The council includes representatives from the industry, programme students, alumni, and others. The students are, through the Halmstad Students’ Union, represented in the university's decision-making bodies as well as in the committees that evaluate programs in accordance with the university's quality system.