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
-------------------- Carlo Pecoraro, Ph.D Physalia-courses DIRECTOR i...@physalia-courses.org mobile: +49 17645230846 [ Bluesky ]( https://bsky.app/profile/physaliacourses.bsky.social ) [ Linkedin ]( https://www.linkedin.com/in/physalia-courses-a64418127/ ) -- https://mail.python.org/mailman/listinfo/python-list
