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 studies at PhD-level.
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 discover 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:
- 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 third area allows the students to deepen their knowledge of 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, the 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.
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.