Online Course: Machine Learning Methods for Longitudinal Data with Python
Dates: 6–9 May 2025
This course introduces methods for analyzing longitudinal (sequence) data—datasets collected repeatedly in time or space—where time and causation are key factors. Participants will learn about challenges in time-series and sequence data analysis, covering both classical statistical approaches and modern machine learning techniques.
The course will explore:
- Time-series forecasting and survival analysis
- Bayesian networks and graph models
- Confounding, colliding, and mediator bias in causal inference
- Deep learning models, including Transformers
- Applications in epidemiology, gene expression, and other life sciences
The course is designed for researchers and professionals working with time-dependent data, particularly in biological sciences. While familiarity with Python is helpful, it is not a requirement. The program includes a mix of lectures, hands-on exercises using Python and Jupyter Notebooks, and discussions of participants’ research challenges.
For more details and registration, visit: https://www.physalia-courses.org/courses-workshops/longitudinal-data-in-r/
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