For students attending the Master's Programme in Biostatistics and Data Science Elective courses

These courses take place in the second and third semesters and allow students to customise their education by studying elective courses that deepen or broaden their knowledge in their areas of interest within biostatistics and data science. No more than 15 credits (of the 30 credits of electives) can be at the undergraduate level. 

Students must choose one (and only one) of the following courses:

• SF2935 Modern Methods of Statistical Learning (7.5 credits)
• DD2421 Machine Learning (7.5 credits)
• MT7049 Statistical Learning (7.5 credits)

Exception: students who have completed the KTH course “DD1420 Foundations of Machine Learning” as part of an earlier degree may not select any of these three courses and must choose a more advanced course in machine/statistical learning from the list below.

All three courses SF2935 (Autumn, first study period), DD2421 (Spring, first study period) and MT7049 (Autumn, second study period) cover statistical aspects of modern methods of machine learning. DD2421 gives a broad overview of such methods, with more focus on algorithms and less required knowledge of statistics and probability theory. SF2935 and MT7049 give a deeper treatment of the statistical aspects of machine learning methods. These two courses have a lot of overlap, with SF2935 requiring less probability and statistics compared to MT7049. Whereas SF2935 covers supervised and unsupervised learning, MT7049 gives a deeper account of supervised learning (nonlinear regression, regularisation, and model selection). Project work is an important part of all three courses. SF2935 typically has a larger class size than MT7049.   

The remaining 22.5 credits are selected from a list of courses (see below). 

Procedures for applying for conditionally elective courses for Autumn 2025

Applications should be submitted during the period 1-15 May 2025.

KTH, SU, and KI have different application periods. We will use the KTH application period (1-15 May) for all three universities. That is, you should ignore any information you see on the SU and KI webpages regarding application periods and deadlines.

For conditionally elective courses at KTH, applications should be submitted via universityadmissions.se. See this page for further information. Information about freestanding courses at KTH can be found here. The KTH master’s coordinators who work with our programme do not work with freestanding courses; contact info@kth.se if you have questions about KTH freestanding courses.

For conditionally elective courses at SU, applications should be submitted by completing an online survey here. The survey will be open 1-15 May. The SU course web pages have a link to apply via universityadmissions; that link should not be used by students in the master's programme in biostatistics and data science.

For conditionally elective courses at KI, applications should be submitted by sending an e-mail to programme officer Markus Jonegård with subject line “BDS elective course application HT2025”.

Note that we use the word “applications”, but BDS students have guaranteed places in the conditional elective courses listed on this page so, in practice, it is a selection rather than an application. As such, do not apply for more courses than you intend to study; there is no need to apply for “backup” courses. Your programme includes a total of 30 credits conditional elective courses. You may not study more than four 7.5 credit elective courses as part of your programme. If you are interested in studying additional courses as “free-standing” courses outside the programme then that should be discussed with a study coordinator.

List of conditional elective courses

The conditionally elective courses listed below are grouped according to the study period in which they start. Each course name is preceded by the corresponding course code and the name of the responsible university (KTH or SU). 

For guidance, we use the following abbreviations:

**: One of the three required “basic” courses in machine/statistical learning
ML: focus on (possibly statistical) machine learning
BS: focus on biostatistics
MS: focus on mathematical statistics 
DS: general data science, not necessarily ML, BS, or MS. 
UG: undergraduate level

 

Spring 2026, first study period (P3)

DD2421 ** (KTH) Machine learning, 7.5 credits (ML)
SF2930 (KTH) Regression analysis, 7.5 credits (MS)
SF2526 (KTH) Numerical algorithms for data intensive science, 7.5 credits (DS)
DD2420 (KTH) Probabilistic Graphical Models, (ML) 7.5 credits
MT7042 (SU) Statistical deep learning, (ML) 7.5 credits

Note: DD2420 is an advanced course in machine learning. It is intended for students who have previously studied basic courses in machine learning (corresponding to SF2935, DD2421, or MT7049) and wish to take this course in place of the compulsory basic course.

Spring 2026, second study period (P4)

MT5020 (SU) The mathematics and statistics of infectious disease outbreaks, 7.5 credits (BS, UG)
SF2943 (KTH) Time series analysis, 7.5 credits (MS)
DD2424 (KTH) Deep learning in data science, 7.5 credits (ML)

Autumn 2025, first study period (P1)

SF2935 ** (KTH) Modern methods of statistical learning, 7.5 credits (ML)
SF2956 (KTH) Topological data analysis, 7.5 credits (DS) 
DD2610 (KTH) Deep learning, advanced course, 7.5 credits (ML). Held over both P1 (4.5 credits) and P2 (3.0 credits)
MT7045 (SU) Bayesian methods, 7.5 credits. (MS) Only held odd years.
SK2532 (KTH) Biomedicine for Engineers, 7.5 credits

Autumn 2025, second study period (P2)

MT7049 ** (SU) Statistical learning, 7.5 credits (ML)
SF2957 (KTH) Statistical machine learning, 7.5 credits (ML)
MT7037 (SU) Statistical Information Theory, 7.5 credits (MS) Only held odd years.
(KI) Mathematics of causal inference, 7.5 credits (BS)
(KI) Fundamentals of Statistical Modelling, 7.5 credits (BS)

Autumn 2026, first study period (P1)

SF2935 ** (KTH) Modern methods of statistical learning, 7.5 credits (ML)
SF2956 (KTH) Topological data analysis, 7.5 credits (DS) 
DD2610 (KTH) Deep learning, advanced course, 7.5 credits (ML). Held over both P1 (4.5 credits) and P2 (3.0 credits)
MT7050 (SU) Unsupervised learning, 7.5 credits (ML). Only held even years. 
SK2532 (KTH) Biomedicine for Engineers, 7.5 credits

Autumn 2026, second study period (P2)

MT7049 ** (SU) Statistical learning, 7.5 credits (ML)
SF2957 (KTH) Statistical machine learning, 7.5 credits (ML)
MT7051 (SU) Reinforcement learning, 7.5 credits (ML). Only held even years. 
(KI) Mathematics of causal inference, 7.5 credits (BS)
(KI) Fundamentals of Statistical Modelling, 7.5 credits (BS)