Machine learning is transforming how complex problems are approached across science, engineering and industry. Recent advances in generative AI, large language models and agentic AI demonstrate both the pace of progress and the growing reach of the field. In this programme, you study important emerging technologies while developing a deep understanding of the principles and methods that underpin modern machine learning. This foundation enables you to critically evaluate and effectively apply current techniques, develop new solutions, and continue adapting as the field evolves.
Building on this foundation, the programme spans probabilistic modelling, optimisation and statistical learning, as well as neural networks, representation learning, deep learning and artificial intelligence. Across different application domains, you examine how the properties of the data and task influence model design, training and evaluation. Depending on your course choices, you can study deep generative modelling in greater depth, including autoregressive models and large language models, diffusion models and other generative approaches. Other options let you investigate robustness, explainability, fairness and privacy in machine learning systems.
Teaching moves between mathematical reasoning, implementation and empirical investigation. You derive and prove selected results, implement methods from mathematical or algorithmic descriptions, and train, tune and evaluate models on data. Laboratory work, programming assignments and projects are combined with seminars and written and oral work, developing your ability to analyse scientific literature, design sound experiments and explain what the evidence supports. The aim is that you can work independently on problems for which neither the appropriate method nor the answer is given in advance.
The programme draws on KTH research environments spanning machine learning, reinforcement learning, computer vision and robotics, speech and music technology, computational science and the theoretical foundations of data science. This gives you contact with both fundamental research and applications, as well as opportunities to engage with current research questions in courses and projects.
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).
Programme structure and progression
During the first year, you build a foundation in machine learning through mandatory courses in machine learning, artificial intelligence and research methodology. These courses provide the basis for more advanced studies later in the programme.
From the second semester onwards, you shape your own profile through elective courses from two broad areas: application domains that use machine learning and theoretical machine learning. Depending on your interests, you can deepen your knowledge in areas such as computer vision, information retrieval, speech and language processing, computational biology, robotics, statistics, optimisation, deep learning and generative AI.
The programme concludes with a degree project carried out during the final semester in an academic or industrial environment in Sweden or abroad. In the past, students from the programme have completed projects at companies such as Saab, Elekta, Flir, Ericsson, Tobii, Spotify, Thales, and AstraZeneca.
Courses in the programme
The courses in the programme cover topics such as machine learning, deep learning, statistical modelling, artificial intelligence, computer vision, speech technology, information retrieval, and optimisation.
Courses in the master's programme in Machine Learning
Meet students from the programme
"The best part is the balance between challenging coursework and interesting ideas. One moment you're wrestling with a complex machine learning concept, the next
| QS 综合 | 院校 | 学制 | 学费 |
|---|---|---|---|
| 第 55 位 | 卡内基梅隆大学Carnegie Mellon University | 两年 | 52,320美元 |