Machine Learning for Physicists and Astronomers
Wouldn't it be nice if the computer could learn how to best analyze your data automatically? It might not be that simple, but in this course you will learn about machine learning, how it can be used and its limitations. Focus is on applications in physics and astronomy.
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.
Information for admitted students spring 2025
Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.
In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.
Follow the instructions on whether you have to reply to your offer or not.
universityadmissions.se
Checklist for admitted students
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Activate your university account
The first step in being able to register and gain access to all the university's IT services.
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Register at your department
Registration can be done in different ways. Read the instructions from your department below.
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Read all the information on this page
Here you will find what you need to know before your course or programme starts.
IMPORTANT
Your seat may be withdrawn if you do not register according to the instructions provided by your department.
Information from the department
Everyone admitted to a course in Physics will receive a welcome letter with important information from us via e-mail. If you have not receive an e-mail by mid-January, please contact our Academic advisor! Unfold and read more.
Roll-call
Courses at the Department of Physics do not have a roll-call. Instead the course starts with the first lecture.
Registration
After being admitted to a course, you must register to confirm that you are starting your studies. For most of our courses this can be done online using your university account. Registration normally opens two weeks before the course starts and you must have registered at the latest one week after. If you have any problems with registration, contact our Student office. Contact details can be found below.
Click here to register online.
Learning platform
Most of the courses in physics use the Athena learning platform. Once registered, the course should appear automatically in Athena. If you cannot find the course, contact the course coordinator. If the course uses a different website, you can find the link further down on this web page.
Conditionally admitted
If you are conditionally admitted to a course at our department you need to contact our Academic advisor before you can register. Contact us as soon as possible, well before the course starts. Contact details are found further down on this web page.
Applicants on waiting list
Are you placed on a waiting list to any of our courses? You will always be contacted via e-mail if you are offered a place. Normaly we will not admit new students if more than 1 week has passed after the first lecture.
Find the Departmend of Physics
Most of the physics courses are held in the AlbaNova building, located between the Frescati campus and the Royal Institute of Technology (Tekniska högskolan, KTH). Courses in medical radiation physics are sometimes held at Campus Karolinska Hospital. A few of our physics courses are also given in collaboration with KTH or other departments. If this is the case it is clearly stated further down on this web page.
Welcome activities
Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below.
Your department may also organise activities for welcoming international students. More information will be provided by your specific department.
Find your way on campus
Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.
Read more
For new international students
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.
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Course structure
This is a second cycle course given at half speed during daytime.
Modules
This course consists of two parts:
* 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.
Teaching format
The teaching consists of lectures, group education and supervision of projects.
Assessment
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.
Examiner
Jens Jasche
Phone: +468 5537 8037
E-mail: jens.jasche@fysik.su.se
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
- Understanding Machine Learning: From Theory to Algorithms, Shai Ben-David and Shai Shalev-Shwartz, Cambridge University Press New York, NY, USA, 2014
- Pattern Recognition and Machine Learning (Information Science and Statistics), Christopher M. Bishop, Springer-Verlag Berlin, Heidelberg, 2006
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, The MIT Press, 2016
Note that online versions of the books are available for free.
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Course reports
Här ligger ett skript.
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More information
When can I apply?
Registration is open from mid-March to mid-April for courses that run in the fall, and from mid-September to mid-October for courses that run in the spring.
Please note that many courses open for late registration in mid-July for courses in the autumn term and in mid-December for courses in the spring term.
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Contact
Course coordinator and teacher:
Jens Jasche, tel: 08 5537 8037, e-mail: jens.jasche@fysik.su.seComputer exercises:
Ludvig Doeser, e-mail: ludvig.doeser@fysik.su.seAcademic advisor at the Department of Physics: studievagledare@fysik.su.se
Student office: studentexp@fysik.su.se
