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comp.lang.python |
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<[email protected]> |
Dear all,
There are still 5 seats left for the upcoming Physalia course "Machine Learning Methods for Longitudinal Data with Python," which is taking place online from 6-9 May. This course will provide a comprehensive introduction to analyzing sequence data (repeated over time or space) when time and causation play a crucial role.
This course will cover both classical statistical and modern machine learning approaches to handling time-dependent data. Participants will learn how to recognize and address temporal dependencies, disentangle cause-effect relationships, and apply appropriate modeling techniques for forecasting, survival analysis, and multi-omics data integration. Topics will include:
Statistical and machine learning methods for sequence data
Bias resolution: confounding, colliding, and mediator biases
Time-series forecasting and predictive modeling
Bayesian networks and graph models
Applications in epidemiology, gene expression, and multi-omics
The course combines lectures, hands-on exercises, and case studies to ensure participants gain practical skills for applying these methods to real-world biological data.
To register or learn more, please visit [ https://www.physalia-courses.org/courses-workshops/longitudinal-data/ ]( https://www.physalia-courses.org/courses-workshops/longitudinal-data/ )
Best regards,
Carlo
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Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
[email protected]
mobile: +49 17645230846
[ Bluesky ]( https://bsky.app/profile/physaliacourses.bsky.social ) [ Linkedin ]( https://www.linkedin.com/in/physalia-courses-a64418127/ )