Master in Machine Learning

General

Program Description

Machine Learning develops algorithms to find patterns or make predictions from empirical data and this master’s program 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 program will be experts in the field, qualified for exciting careers in industry or doctoral studies.

Machine Learning at KTH

In this program, you will learn the mathematical and statistical foundations and methods for machine learning with the goal of modeling 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 program, you will have gained the confidence and experience to propose tractable solutions to potentially non-standard learning problems that 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 program 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:

  • DD2424 Deep Learning in Data Science
  • DD2423 Image Analysis and Computer Vision
  • DT2119 Speech and Speaker Recognition
  • DD2437 Artificial Neural Networks and Deep Architectures
  • DD2425 Robotics and Autonomous Systems

The program also has 30 ECTS credits of elective courses which you can choose from a wide range of courses to specialize 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, the student gets to demonstrate their ability to perform independent project work, using the skills obtained from the courses in the program. In the past students from the program have completed projects at companies such as Saab, Elekta, Flir, Eriksson, Tobii, Spotify, Thales, Huawei.

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

Career

The demand for engineers and scientists with 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 program is also a suitable basis for work in a research and development department in the industry, as well as for a continued research career, and doctoral studies.

Students

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

Andres Alonso Toledo Carrera, Mexico: "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."

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 programs. The three key sustainable development goals addressed by the master's program in Machine Learning are:

  • Good Health and Well-Being
  • Sustainable Cities and Communities
  • 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-personalized medicines and diagnosis. Some of these developments will only benefit society while others may not. As graduates of this program, 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 program, 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 program 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.

Courses

The two-year master's program in Machine Learning consists of three terms of courses and one final term dedicated to the master's degree project. Each term consists of approximately 30 ECTS credits. The courses presented on this page apply to studies starting in autumn 2020.

Year 1

Mandatory courses

  • Introduction to the Philosophy of Science and Research Methodology (DA2205) 7.5 credits
  • Program Integrating Course in Machine Learning (DD2301) 3.0 credits
  • Artificial Intelligence (DD2380) 6.0 credits
  • Machine Learning (DD2421) 7.5 credits
  • Machine Learning, Advanced Course (DD2434) 7.5 credits

Conditionally elective courses

  • Visualization (DD2257) 7.5 credits
  • Neuroscience (DD2401) 7.5 credits
  • Advanced Individual Course in Computational Biology (DD2402) 6.0 credits
  • Introduction to Robotics (DD2410) 7.5 credits
  • Research project in Robotics, Perception, and Learning (DD2411) 15.0 credits
  • Deep Learning, Advanced Course (DD2412) 6.0 credits
  • Language Engineering (DD2418) 6.0 credits
  • Probabilistic Graphical Models (DD2420) 7.5 credits
  • Image Analysis and Computer Vision (DD2423) 7.5 credits
  • Deep Learning in Data Science (DD2424) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Computational Photography (DD2429) 6.0 credits
  • Mathematical Modelling of Biological Systems (DD2435) 9.0 credits
  • Artificial Neural Networks and Deep Architectures (DD2437) 7.5 credits
  • Artificial Intelligence and Multi-Agent Systems (DD2438) 15.0 credits
  • Statistical Methods in Applied Computer Science (DD2447) 6.0 credits
  • Search Engines and Information Retrieval Systems (DD2476) 9.0 credits
  • Speech Technology (DT2112) 7.5 credits
  • Speech and Speaker Recognition (DT2119) 7.5 credits
  • Applied Estimation (EL2320) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Pattern Recognition and Machine Learning (EQ2341) 7.5 credits
  • Data Mining (ID2222) 7.5 credits
  • Scalable Machine Learning and Deep Learning (ID2223) 7.5 credits
  • Optimization (SF1811) 6.0 credits
  • Regression Analysis (SF2930) 7.5 credits
  • Probability Theory (SF2940) 7.5 credits
  • Time Series Analysis (SF2943) 7.5 credits

Recommended courses

  • Program System Construction Using C++ (DD1388) 7.5 credits
  • Algorithms and Complexity (DD2352) 7.5 credits
  • Computer Security (DD2395) 6.0 credits
  • Foundations of Cryptography (DD2448) 7.5 credits
  • Interaction Programming and the Dynamic Web (DH2642) 7.5 credits
  • Logic Programming (ID2213) 7.5 credits
  • Data-Intensive Computing (ID2221) 7.5 credits
  • Parallel Computations for Large- Scale Problems (SF2568) 7.5 credits

Year 2

Mandatory courses

  • Degree Project in Computer Science and Engineering, specializing in Machine Learning, Second Cycle (DA233X) 30.0 credits
  • Program Integrating Course in Machine Learning (DD2301) 3.0 credits

Conditionally elective courses

  • Visualization (DD2257) 7.5 credits
  • Introduction to Robotics (DD2410) 7.5 credits
  • Research project in Robotics, Perception, and Learning (DD2411) 15.0 credits
  • Deep Learning, Advanced Course (DD2412) 6.0 credits
  • Probabilistic Graphical Models (DD2420) 7.5 credits
  • Image Analysis and Computer Vision (DD2423) 7.5 credits
  • Robotics and Autonomous Systems (DD2425) 9.0 credits
  • Project Course in Data Science (DD2430) 7.5 credits
  • Mathematical Modelling of Biological Systems (DD2435) 9.0 credits
  • Artificial Neural Networks and Deep Architectures (DD2437) 7.5 credits
  • Artificial Intelligence and Multi-Agent Systems (DD2438) 15.0 credits
  • Statistical Methods in Applied Computer Science (DD2447) 6.0 credits
  • Applied Estimation (EL2320) 7.5 credits
  • Reinforcement Learning (EL2805) 7.5 credits
  • Data Mining (ID2222) 7.5 credits
  • Scalable Machine Learning and Deep Learning (ID2223) 7.5 credits
  • Optimization (SF1811) 6.0 credits
  • Regression Analysis (SF2930) 7.5 credits
  • Probability Theory (SF2940) 7.5 credits

Recommended courses

  • Program System Construction Using C++ (DD1388) 7.5 credits
  • Algorithms and Complexity (DD2352) 7.5 credits
  • Computer Security (DD2395) 6.0 credits
  • Foundations of Cryptography (DD2448) 7.5 credits
  • Interaction Programming and the Dynamic Web (DH2642) 7.5 credits
  • Logic Programming (ID2213) 7.5 credits
  • Data-Intensive Computing (ID2221) 7.5 credits
  • Parallel Computations for Large- Scale Problems (SF2568) 7.5 credits

Admission requirements

To be eligible for the program, you must have been awarded a bachelor's degree, be proficient in English and meet the program-specific requirements.

Bachelor's degree

A bachelor's degree, equivalent to a Swedish bachelor's degree, or equivalent academic qualifications from an internationally recognized university, is required. Students who are following longer technical programs, and have completed courses equivalent to a bachelor's degree, will be considered on a case-by-case basis.

English proficiency

English language proficiency equivalent to (the Swedish upper secondary school) English course B/6 is required. The requirement can be satisfied through a result equal to, or higher than, those stated in the following internationally recognized English tests:

  • TOEFL Paper-based: Score of 4.5 (scale 1-6) in written test, a total score of 575.
    TOEFL ITP is not accepted.
  • TOEFL iBT internet-based: Score of 20 (scale 0-30) in written test, a total score of 90
  • IELTS Academic: A minimum overall mark of 6.5, with no section lower than 5.5
  • Cambridge ESOL: Cambridge English: Advanced (CAE) Certificate in Advanced English or Cambridge English: Proficiency (CPE) (Certificate of Proficiency in English)
  • Michigan English Language Assessment Battery (MELAB): Minimum score of 90
  • The University of Michigan, ECPE (Examination for the Certificate of Proficiency in English)
  • Pearson PTE Academic: Score of 62 (writing 61)

Specific requirements for the master's program in Machine Learning

A Bachelor’s degree, or equivalent, corresponding to 180 ECTS credits, with a level in Mathematics and Computer Science equal to, or higher than, that of the following courses at KTH:

  • SF1624 Algebra and geometry
  • SF1625 Calculus in one variable
  • SF1626 Calculus in several variables
  • SF1901 Probability theory and statistics
  • DD1337 Programming
  • DD1338 Algorithms and Data Structures

Application documents

  1. Certificates and diplomas from previous university studies
  2. Transcript of completed courses and grades included in your degree
  3. Proof of English proficiency
  4. A copy of your passport including personal data and photograph, or other identification documents

Specific documents for the master's program in Machine Learning

  • Letter of motivation
  • Letters of recommendation
  • Summary sheet *

*In order for your application to be considered complete, you need to fill out the online summary sheet. If you do not include a summary sheet, this may negatively affect your evaluation score. Please be sure to fill out all of the required information before you submit the form.

Last updated Apr 2020

About the School

KTH Royal Institute of Technology has served as one of Europe’s key centres of innovation and intellectual talent for almost two hundred years. Recognized as Sweden’s most prestigious technical univer ... Read More

KTH Royal Institute of Technology has served as one of Europe’s key centres of innovation and intellectual talent for almost two hundred years. Recognized as Sweden’s most prestigious technical university, KTH is also the country’s oldest and largest. With over 12,000 students and an international reputation for excellence, the university continues to nurture the world’s brightest minds, helping to shape the future. Read less