Master's programme in Machine Learning

The scientific discipline of Machine Learning focuses on developing algorithms to find patterns or make predictions from empirical data. The discipline is increasingly used by many professions and industries (for example manufacturing, retail, medicine, finance, robotics, telecommunications), as it can help create order in large amounts of digital data to solve difficult problems such as understanding human behaviour and providing efficient resource allocation. Demand for graduates with substantive expertise in machine learning far exceeds supply. The programme here at KTH equips you for a career in industry (a start-up or a traditional well-established company) and will also prepare you for further doctoral studies.

Application closed

The application for the programme is now closed.

16 October 2017: Application opens
15 January 2018: Application deadline
1 February 2018: Deadline for supporting academic documents (all applicants) and documentation of fee exempt status (if required) or receipt of application fee (if required)
6 April 2018: Notification of selection results

Non-EU/EEA/Swiss citizens: The tuition fee for the full programme is SEK 310,000
Non-EU/EEA/Swiss citizens are generally required to pay an application fee of SEK 900.

EU/EEA/Swiss citizens: There are no tuition fees for EU/EEA/Swiss citizens
EU/EEA/Swiss citizens are not required to pay an application fee.

Read more about tuition and application fees

Degree awarded: Master of Science
Language of instruction: English
Duration: Two years (120 ECTS credits)
Programme start: Late August
Location: KTH Campus, Stockholm
School: School of Electrical Engineering and Computer Science (at KTH)

For questions regarding programme content and specific admission requirements, feel free to contact the programme coordinators.

Programme coordinator team:

Machine Learning at KTH

In this programme you will learn the mathematical and statistical foundations and methods for machine Learning with the goal of modelling and discovering patterns from observations. You will also gain practical experience of how to match, apply and implement relevant ML techniques to solve real world problems in a large range of application domains. Upon graduation from the programme you will have gained the confidence and experience to propose tractable solutions to potentially non-standard learning problems which you can implement efficiently and robustly.

The programme starts with compulsory courses in machine learning, artificial intelligence, an advanced course in machine learning and research methodology, which provide an introduction and solid foundation to the field. From the second term, students choose courses from three areas: application domains within machine learning, applied mathematics/statistics, and computer science. These areas correspond to the core competencies of a machine learning expert.

The first area describes how machine learning is used to solve problems in particular application domains such as computer vision, information retrieval, speech and language processing, computational biology and robotics. The second area gives the students the chance to take more basic theoretical courses in applied mathematics, statistics, and machine learning. Of particular interest to many will be the chance to learn about and understand in detail the exciting field of deep learning through several state-of-the-art courses such as:

The third area allows the students to deepen their knowledge in theoretical computer science, software engineering, and parallel computing.

The programme also has 30 ECTS credits of elective courses which you can choose from a wide range of courses to specialise further in your field of interest, or extend your knowledge to new areas within machine learning.

The final term is dedicated to a degree project which involves participating in advanced research or design projects in an academic or industrial environment, in Sweden or abroad. With this project, student gets to demonstrate their ability to perform independent project work, using the skills obtained from the courses in the programme. In the past students from the programme have completed projects at companies such as Saab, Elekta, Flir, Eriksson, Tobii, Spotify, Thales, Huawei.

Topics covered

Machine learning, deep learning, statistical modelling, artificial intelligence, computer vision, speech technology, information retrieval, optimization



The demand for engineers and scientists with a knowledge in Machine Learning is growing as the amount of data in the world increases. After graduation you can pursue a career, for example as a software developer, deep learning engineer, computer vision engineer, data analyst, software engineer, quantitative analyst, data scientist, and systems engineer for companies as Dice, Logitech, Google, and McKinsey in, for example, Sweden, Switzerland, Germany, China, India, and the US.

This master's programme is also a suitable basis for work in a research and development department in industry, as well as for a continued research career, and doctoral studies.

After graduation

Software developer, deep learning engineer, computer vision engineer, data analyst, software engineer, quantitative analyst, data scientist, and systems engineer


Find out what students from the programme think about their time at KTH.

Ruibo Tu, China

"I think KTH is the only university in Europe that has a programme focusing on machine learning."

Meet the students

Changes in the programme may occur.

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