Machine Learning for Physicists and Astronomers

About this programme

Machine learning is one of the fastest growing and most dynamic areas of modern physics research and data application. In this course you will get an introduction to the core concepts, theory and tools of machine learning as required by physicists and astronomers addressing practical data analysis tasks. Use cases and limitations of machine learning algorithms will be discussed. The implementation and use of machine learning in practical applications will be exemplified, and realistic scenarios will be studied in applications relevant to physics research and astronomy. * Theory. In this part, the theory of machine learning and different models of it will be studied. You will also learn about how to choose which method for your given problem and the limitation of different methods. * Project. In this part, you will implement and use machine learning for data analysis. You will also learn how to prepare data and train machine learning models and evaluate the performance and quality of them. The theory part is examined by a written and oral exam. The project part is examined through a written and oral presentation of the project work. To follow your passion. To be close to international career opportunities. To explore city and countryside. In Stockholm you can really get the best of both worlds. Here we explain all the different steps you need to go through when applying to a course or programme. Stockholm University arranges regular webinars and participates in educational fairs and events virtually and around the world to meet students and inform them about our study