Syllabus for Master's Programme in Image Analysis and Machine Learning
Masterprogram i bildanalys och maskininlärning
- 120 credits
- Programme code: TBA2M
- Established: 2019-10-22
- Established by: The Faculty Board of Science and Technology
- Revised: 2020-11-11
- Revised by: The Faculty Board of Science and Technology
- Reg. no: TEKNAT 2020/258
- Syllabus applies from: Autumn 2021
- Responsible faculty: Faculty of Science and Technology
- Responsible department: Department of Information Technology
A Bachelor's degree, equivalent to a Swedish Kandidatexamen, from an internationally recognised university.
Also required is:
- 80 credits in mathematics and computer science; out of which
- 30 credits in mathematics including linear algebra, single variable calculus, statistics and probability; and in addition
- 30 credits in computer science including 5 credits in introductory programming.
All applicants need to verify English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6"). This can be done in a number of ways, including through an internationally recognised test such as TOEFL or IELTS, or through previous upper secondary (high school) or university studies.
The minimum test scores are:
- IELTS: an overall mark of 6.5 and no section below 5.5
- TOEFL: Paper-based: Score of 4.5 (scale 1–6) in written test and a total score of 575. Internet-based: Score of 20 (scale 0–30) in written test and a total score of 90
- Cambridge: CAE, CPE
Decisions and Guidelines
According to a decision taken by the Vice Chancellor 2019-06-18, Uppsala University will offer a Master's Programmes in Image Analysis and Machine Learning from 2020-07-01.
The Master's Programme in Image Analysis and Machine Learning focuses on the latest groundbreaking advances in image analysis and processing, which are based on modern methods of deep and machine learning developed for visual data. The programme aims to meet the increased need for knowledge and skills in this particular combination of subjects and defines a new professional profile that corresponds to the growing shortage of expertise in analysis, processing and interpretation of images and video that prevails in both academia and industry. The programme paves the way for a career in industry, in many companies that are in need of skills in deep and machine learning, as well as image and video analysis, or in academia and other research-intensive workplaces.
The programme consists of a carefully selected combination of courses that provide both a strong theoretical foundation and an ability to apply this knowledge in practice. The programme offers courses, project work, and specializations, in collaboration with industrial and academic partners. This provides the conditions for relating acquired knowledge and skills to relevant and current problems in both research and industry.
According to the Higher Education Act, the following applies for second-cycle studies:
Second-cycle studies shall be based fundamentally on the knowledge acquired by students during first-cycle courses and study programmes, or its equivalent. Second-cycle studies shall involve the acquisition of specialist knowledge, aptitudes and accomplishments in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall:
- further develop the ability of students to integrate and make autonomous use of their knowledge,
- develop the students' ability to deal with complex phenomena, issues and situations, and
- develop the students' potential for professional activities that demand considerable autonomy, or for research and development work. Ordinance (2006:173).
Knowledge and understanding
For a Degree of Master (120 credits) students must:
- demonstrate knowledge and understanding in their main field of study, including both broad knowledge in the field and substantially deeper knowledge of certain parts of the field, together with deeper insight into current research and development work; and
- demonstrate deeper methodological knowledge in their main field of study.
- demonstrate knowledge and understanding of principles, methods, and algorithms for image analysis and machine learning, their application and limitations;
- demonstrate in-depth methodological knowledge within image analysis and machine learning and in one of the program's specialization areas.
For a Degree of Master (120 credits) students must:
- demonstrate an ability to critically and systematically integrate knowledge and to analyse, assess and deal with complex phenomena, issues and situations, even when limited information is available;
- demonstrate an ability to critically, independently and creatively identify and formulate issues and to plan and, using appropriate methods, carry out advanced tasks within specified time limits, so as to contribute to the development of knowledge and to evaluate this work;
- demonstrate an ability to clearly present and discuss their conclusions and the knowledge and arguments behind them, in dialogue with different groups, orally and in writing, in national and international contexts; and
- demonstrate the skill required to participate in research and development work or to work independently in other advanced contexts.
- demonstrate the ability to integrate theory and methodology of image analysis and machine learning, and to use, compare, and evaluate different models in realistic problem situations;
- demonstrate the ability to critically, independently and creatively identify and formulate problems where image analysis and machine learning can be applied, to plan and execute advanced tasks within a given framework, and to use appropriate mathematical models, tools and software;
- demonstrate the ability to clearly present, explain, and discuss - orally and in writing - advanced topics in machine learning and image analysis, in dialogue with different groups;
- demonstrate skills required to participate in research and development or to work independently in other advanced contexts, utilizing competence from the subject areas of machine learning and image analysis.
For a Degree of Master (120 credits) students must:
- demonstrate an ability to make assessments in their main field of study, taking into account relevant scientific, social and ethical aspects, and demonstrate an awareness of ethical aspects of research and development work;
- demonstrate insight into the potential and limitations of science, its role in society and people's responsibility for how it is used; anddemonstrate an ability to identify their need of further knowledge and to take responsibility for developing their knowledge.
- demonstrate the ability to make judgments with regard to relevant scientific, social and ethical aspects of applications of machine learning and image analysis, and demonstrate awareness of ethical aspects of research and development in the subject;
- demonstrate insight into the possibilities and limitations of machine learning and image analysis, its role in society and people's responsibility for how they are applied;
- demonstrate ability to identify own needs for further knowledge in the field of machine learning and image analysis and to take responsibility for their own learning and development.
Layout of the Programme
The programme starts by providing students with different backgrounds a common foundation, as well as basic knowledge in digital image analysis, which is one of the programme's two pillars. It continues with introducing basics of machine learning, which is the second pillar the programme. These two subjects define, at an early stage, the identity of the programme, which is further developed and deepened by the education on deep machine learning which also clarifies the strong link between machine learning and modern image processing and analysis. A course in ethics is included in the programme at an early stage. Courses that provide theoretical in-depth and progression towards specialisations and where the subjects image analysis and machine learning are linked together and form the programme's main area follow. Students are given a possibility to specialize within one of the following application fields where the combination of image analysis and machine learning has a central role and where they can further develop their ability to apply in practice acquired theoretical knowledge:
- medical image analysis,
- biomedical image analysis,
- document analysis and digital humanities,
- scientific visualisation,
- social robotics.
The master's programme builds on the experience and knowledge that the students bring into the education. Students are expected to participate in teaching and actively contribute to it, as well as to take a significant responsibility for their own, and for their fellow students' learning. The teachers have main responsibility for creating conditions favouring active individual and joint learning. The teaching approach is designed and developed continuously through a respectful dialogue between teachers and students, where everyone contributes to the renewal and mutual learning.
The programme supports student-active and student-centred learning. The courses combine several different forms of teaching, such as lectures, practical assignments, seminars, communication training, study visits and project work. A large part of the learning takes the form of practical exercises and project assignments, where the students themselves take an active role in their own and their fellow students' learning. These practical parts are supplemented by lectures and seminars aimed at a deeper theoretical understanding of the lessons learned in the practical parts. The students are given concrete experience of combining theoretical understanding with practical problem solving in order to be able to meet the requirements of industrial and academic careers after graduation. The teaching is closely related and inspired by current research, which gives a good insight into the scientific approaches and work. The teaching and course literature is in English.
Upon request, the Vice Chancellor issues diplomas for the Master of Science (120 credits) with Image Analysis and Machine Learning as the main field of study.
A Degree of Master is a so called general degree, which means that the student achieve the degree in its main subject according to the criteria below, regardless of the courses being part of the program or not, therefore there is a possibility also to include single subject courses in the degree.
Regulations according to Higher Education Ordinance
A Degree of Master (120 credits) is obtained after the student has completed course requirements of 120 higher education credits with a certain area of specialisation determined by each higher education institution itself, including at least 60 higher education credits with in-depth studies in the main field of study. In addition, the student must hold a Degree of Bachelor, a Degree of Bachelor of Arts in…, a professional degree worth at least 180 higher education credits or an equivalent foreign qualification.
For a Degree of Master (120 credits) students must have completed an independent project (degree project) worth at least 30 higher education credits in their main field of study.
A degree of Master (120 credits) may, except for courses on advanced level, contain one or several courses on basic level comprising not more than 30 higher education credits. The course or the courses are meant to provide such additional competence as is needed for in-depth studies in the main field of study and cannot be included in the student's basic degree.
For a Degree of Master (120 credits) students must have completed an independent project (degree project) worth at least 30 higher education credits.
To be accepted to the later part of the programme the student must have gained at least 15 credits of programme-relevant courses in addition to the degree at Bachelor's level outside the study programme. The application deadline for admission to the later part of the programme is 1 May for the autumn semester and 1 December for the spring semester.
- Latest syllabus (applies from Autumn 2021)
- Previous syllabus (applies from Autumn 2020)