Biostatistics and Machine Learning II
November 25, 2024 @ 09:00 – November 29, 2024 @ 17:00 CET
National course for PhD students, researchers, and other employees across Swedish universities who seek to deepen their biostatistical and machine learning skills. Building on the Introduction to Biostatistics and Machine Learning course, this course expands on common life science data analysis methods, including dimensionality reduction techniques beyond PCA, mixed-effects models for analysis of repeated measures, and survival analysis. We will also dive deeper into machine learning, covering more classification algorithms, ensemble techniques, optimization strategies and PLS methods for single and multi-omics data analysis.
Important dates
Application open: now
Application closes: 2024-10-18
Confirmation to accepted students: 2024-10-25
Responsible teachers: Payam Emami, Olga Dethlefsen, Eva Freyhult
If you do not receive information according to the above dates please contact edu.ml-biostats@nbis.se
Course fee
A course fee of 3000 SEK for academic participants and 15 000 SEK for non-academic participants will be invoiced to accepted participants. The fee includes lunches, coffee and snacks.
*Please note that NBIS cannot invoice individuals
Course content
- Dimensionality reduction techniques beyond PCA
- Classification algorithm and ensemble techniques
- Machine learning optimization strategies
- PLS-based methods for single and multi-omics data analysis
- Mixed-effects models for repeated measures, longitudinal studies and nested designs
- Survival analysis
- Introduction to neural networks
Education
In this course, we focus on an active learning approach. The education consists of teaching blocks alternating between lectures, group discussions, live coding sessions etc.
Entry requirements
- Basic R and Python data science skills (for more details see course website)
- Having attended the Introduction to Biostatistics and Machine Learning course or having equivalent knowledge
- BYOL (bring your own laptop)
The course can accommodate a maximum of 24 participants. If we receive more applications, participants will be selected based on several criteria. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance.
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