Dear all,

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 focuses on machine learning techniques for analyzing
 longitudinal (sequence) data   data collected repeatedly over
 time or space   with an emphasis on handling time and cause-effect
 relationships. You will learn how to manage the unique challenges posed
 by these data, from visualization and modeling to interpreting results.

 By the end of the course, you will:
Understand how to manage time and cause dependencies in data Recognize
and address biases such as confounding, colliding, and mediator bias
Apply machine learning techniques to sequence data Use graph models,
Bayesian networks, and time-series forecasting Analyze real-life
biological datasets, including epidemiology and gene expression data
 This course is designed for students, researchers, and professionals
 working with biological data   particularly those analyzing time-based
 or causation-driven datasets. A basic understanding of Python and Linux
 is helpful but not required.

Course Schedule (Berlin time): Day 1 (2-8 PM): Introduction to sequence
data, statistical models, handling biases Day 2 (2-8 PM): Graph models,
Bayesian networks, machine learning 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 quiz and
discussion For the full list of our courses and workshops, please visit:
( https://www.physalia-courses.org/courses-workshops/longitudinal-data/ )

For any questions or further information, feel free to get in touch.
Best regards, Carlo

Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
info@physalia-courses.org
mobile: +49 17645230846
( https://www.linkedin.com/in/physalia-courses-a64418127/ )

"info@physalia-courses.org" <info@physalia-courses.org>

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