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 Spring 2025

Students in the Master's Programme in Biostatistics and Data Science should apply between 1st November and 15th November, 2024. 

For conditionally elective courses at KTH, applications should be submitted via universityadmissions.se

For conditionally elective courses at SU, applications should be submitted by completing the survey at https://survey.su.se/NBIDM-V25 between November 1 and November 15. The SU course web pages have a link to apply via universityadmissions.se with an application deadline in October; that link should not be used by students in the master's programme in biostatistics and data science.

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:

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 2025, first study period

DD2421 (KTH) Machine learning, 7.5 credits (ML)
SF2930 (KTH) Regression analysis, 7.5 credits (MS)
MT7051 (SU) Reinforcement learning, 7.5 credits (ML) 
SF2526 (KTH) Numerical algorithms for data intensive science, 7.5 credits (DS)

Spring 2025, second study period

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)
DD204 (KTH) Neuroscience, 7.5 credits

Autumn 2025, first study period (preliminary)

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)
MT7045 Bayesian methods, 7.5 credits

Autumn 2025, second study period (preliminary)

MT7049 (SU) Statistical learning, 7.5 credits (ML)
SF2957 (KTH) Statistical machine learning, 7.5 credits (ML)
MT7037 Statistical Information Theory, 7.5 credits