Master's programme in Machine Learning

Machine Learning develops algorithms to find patterns or make predictions from empirical data and this master’s programme will teach you to master these skills. Machine Learning is increasingly used by many professions and industries such as manufacturing, retail, medicine, finance, robotics, telecommunications and social media. Graduates from the programme will be experts in the field, qualified for exciting careers in industry or doctoral studies.

Application dates for studies starting in Autumn 2020

16 October 2019: Application opens
15 January 2020: Application deadline
3 February 2020: Deadline for supporting academic documents (all applicants) and documentation of fee exempt status (if required) or receipt of application fee (if required)
3 April 2020: Notification of selection results
August 2020: Arrival and start of studies

 

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 machine learning 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. Stockholm has a vibrant start-up community and large established companies integrating AI and Machine Learning into their technological development. This gives you a large potential for relevant and interesting industrial work within the field during and after your studies. 

To provide an introduction to the field and a solid foundation the programme starts with compulsory courses in machine learning and artificial intelligence. These courses are followed by an advanced course in machine learning and research methodology. From the second term, students choose courses from two areas: application domains within machine learning, and theoretical machine learning. These areas correspond to the core competencies of a machine learning expert.

The first grouping of courses 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 course grouping 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 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.

This is a two year programme (120 ECTS credits) given in English. Graduates are awarded the degree of Master of Science. The programme is given mainly at KTH Campus in Stockholm by the School of Electrical Engineering and Computer Science (at KTH). 

Topics covered

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

Courses in the programme

Career

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 in industry, at a start-up or in a traditional well-established company. Possible titles are software developer, deep learning engineer, computer vision engineer, data analyst, software engineer, quantitative analyst, data scientist, and systems engineer in 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.

Meet the graduates

Students

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

"Although I am used to working in teams with projects and assignments, I’ve never worked in an environment as diverse as that at KTH, sharing similar objectives with my classmates but sometimes with different perspectives and methodologies."

Andres Alonso Toledo Carrera, Mexico

Meet the students

Sustainable development

Graduates from KTH have the knowledge and tools for moving society in a more sustainable direction, as sustainable development is an integral part of all programmes. The three key sustainable development goals addressed by the master's programme in Machine Learning are:

3. Good Health and Well-Being
11. Sustainable Cities and Communities
16 Peace, Justice and Strong Institutions

Developments in Machine Learning have begun to permeate many aspects of our life and it is predicted to have an increasingly profound effect on society, for example making many blue and white collar jobs obsolete due to increased automation or improving patient outcomes due to better personalised medicines and diagnosis. Some of these developments will only benefit society while others may not. As graduates of this programme, you will be very well informed about the technical capabilities and potential applications of Machine Learning, as well as being well-positioned to push the advancement of Machine Learning/AI even further. Thus as part of the programme, as well as within KTH, we highlight the ethical issues and responsibilities that will come with these skills and knowledge in mandatory courses such as DD2301 and DD2380. We see these responsibilities as being aligned with the UN Sustainable Development Goals, where we specifically promote awareness of the SDGs as part of “DD2301: the Programme Integration Course" and also highlight the use cases of “AI for good", which intersect with the SDGs, such as in the design and operation of wind and solar farms to make them more efficient, the diagnosis and treatment of various diseases and the design of health interventions, and precision engineering to promote more efficient farming practices.

In the final year of their studies, students from the programme will have an opportunity to complete final degree projects that are highly relevant to multiple SDGs. Examples of where such projects took place in the past are:

  • SDG: “Good Health and Well-being", with medical technology companies such as Elekta and RaySearch;
  • SDG: “Sustainable Cities and Communities", with the automatic monitoring of satellite imagery within the Division of Geoinformatics, KTH.
  • SDG: “Peace and Justice Strong Institutions", with the independent international institute SIPRI.

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