We are pleased to announce the Physalia online course: Machine Learning Methods for Longitudinal Data with Python.
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/
