Dear all,
there are only 2 seats left for the Physalia online course: Machine Learning Methods for Longitudinal Data.
Dates: 6th-9th May
Course website: https://www.physalia-courses.org/courses-workshops/longitudinal-data/
This course will introduce machine learning methods for analyzing longitudinal (sequence) data, where time and cause-effect relationships are important. You will learn how to handle the specific challenges of working with this type of data, from visualization and modeling to interpretation.
Course Highlights:
- Understand how time and causation affect data analysis
- Learn to identify and address biases such as confounding and mediator effects
- Apply machine learning methods to sequence data
- Use graph models, Bayesian networks, and time-series forecasting
- Work with real-world biological datasets, including epidemiology and gene expression
Who Should Attend?
This course is designed for advanced students, researchers, and professionals working with biological data that changes over time. A basic understanding of Python and Linux is helpful but not required.
Course Format:
The course is structured over four days and includes lectures, discussions, and hands-on practical exercises using Python, Jupyter Notebooks, and the Linux command line. Participants will work on exercises, interpret results, and discuss their own research questions.
Schedule (Berlin time):
Day 1 (2-8 PM): Introduction to sequence data, statistical models, bias handling
Day 2 (2-8 PM): Graph models, Bayesian networks, ML approaches for time-series prediction
Day 3 (2-8 PM): Longitudinal data in epidemiology, deep learning, Transformer models
Day 4 (2-8 PM): Model diagnostics, multi-omics case study, final discussion
For the full list of our courses and workshops, please visit: https://www.physalia-courses.org/courses-workshops/
