Do you want to contribute to the visual intelligence of the machines of tomorrow? Join the Master's Programme in Image Analysis and Machine Learning! By specialising in the twin fields of image analysis and machine learning, you will gain the skills to help build a better world through pattern recognition and visual data processing in areas like medicine, life sciences, the humanities, robotics, astronomy, material science and security.
Why this programme?
The Master's Programme in Image Analysis and Machine Learning gives you a comprehensive understanding of its two main subjects, both from a practical and theoretical perspective. The skills you learn will make you an attractive candidate for positions such as software engineer and developer, researcher or project manager after your graduation.
The potential applications of this skill set are vast, and may benefit society in a large variety of ways. You can help medical doctors plan surgeries to fit each individual patient, support them in detecting and curing cancer or rare diseases, assist historians and archaeologists in analysing huge archives of historical documents, or contribute to the development of robots who can understand human feelings.
During the programme you can expect to:
learn about modern image analysis techniques and their applications,
gain a robust and comprehensive understanding of machine learning from both a practical and theoretical perspective,
work in close proximity to leading experts and researchers in the field.
Student profile You are someone with not only a theoretical foundation in computer science and mathematics, but also with interest in developing intelligent machines which can help humans through efficient processing of visual data in a variety of real-life uses.
The programme leads to a Master of Science (120 credits) with Image Analysis and Machine Learning as the main field of study.
The first semester offers basic courses in both image analysis and machine learning, with the two fields becoming increasingly entwined as the courses progress.
The second semester will offer advanced theoretical studies that build upon the courses of the first semester. You will also learn about deep convolutional neural networks, a state-of-the-art machine learning technology which has become central in modern image processing and analysis.
During the third semester, you will gain practical experience by applying your knowledge in an area of your choosing. These areas include medical and biomedical image analysis, document analysis and digital humanities, scientific visualisation and social robotics. The semester also includes a teamwork project that integrates a range of different skills and abilities.
An extensive degree project makes up the majority of the fourth and final semester, where you will get a chance to apply your freshly gained knowledge as part of a relevant project in the industrial or healthcare sector or at an academic research unit, addressing real societal needs.
Students will be encouraged to participate and actively contribute to teaching sessions while also taking responsibility for their own learning.
Instruction consists of lectures, teacher-supervised tuition, practical assignments, seminars, communication training, teamwork and projects. On a seminar, you present your ideas and discuss with your classmates regarding a course book or other study material that you are required to read before the seminar; while the teacher usually only moderates the discussion. The aim is to develop critical thinking and collaborative skills.
All the students are expected to be active participants in all forms of discussions. For teamwork projects, you will need to complete the work together with your classmates outside the classroom. This way, you learn from each other and you train to be a team player.
The programme is intimately tied to contemporary research, and the courses closely follow current developments in image analysis, machine learning, and artificial intelligence.
Upon completion of your studies, you will be qualified for a range of different positions. You will have the opportunity to apply your skills in the industrial sector, for instance in software engineering, software development or project management within machine learning, artificial intelligence, image analysis, data mining and big data.
The AI Index 2018 Annual Report found that within the AI field, machine learning is the most commonly requested qualification in job postings. Deep machine learning was also found to be the fastest growing qualification, with computer vision in second place. Around 70 000 new companies are created in Sweden every year, and many of these will offer exciting job opportunities for graduates of this programme. You may also be interested in starting your own company within the field. You will also have the option to remain in academia and pursue a PhD.
Image analysis and machine learning are highly active and popular research areas, and students with degrees from the Master's programme at Uppsala University are ideal candidates for PhD positions in the field.
Career support During your whole time as a student UU Careers offers you support and guidance. You have the opportunity to partake in a variety of career activities and events, as well as receive individual career counselling. This service is free of charge for all students at Uppsala University. Read more about UU Careers.
Below you will find the details about eligibility requirements, selection criteria, and tuition fees. For information on how to apply and what documents you need to submit, check the application guide. For this programme, besides the general supporting documents, you also need to submit two programme-specific documents: 1. a CV; 2. an Application Summary Sheet.
Master's Programme in Image Analysis and Machine Learning
Requirements: Academic requirements 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.
Language requirements Proficiency in English equivalent to the Swedish upper secondary course English 6. This requirement can be met either by achieving the required score on an internationally recognised test, or by previous upper secondary or university studies in some countries. Detailed instructions on how to provide evidence of your English proficiency are available at universityadmissions.se.
Students are selected based on:
an overall appraisal of previous university studies,
a curriculum vitae, and
a statement of purpose to be included in the application summary sheet.
Tuition fee-paying students and non-paying students are admitted on the same grounds but in different selection groups.
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees. Fees cover application and tuition only and do not cover accommodation, academic literature or the general cost of living. Read more about fees.