Subject: =?utf-8?Q?Machine_Learning_Methods_for_Longitudinal_Data_with_Python_?=
=?utf-8?Q?=E2=80=93_Online_Course_=286-9_May=29?=

=0ADear all,=0AThere are still 5 seats left for the upcoming Physalia cours=
e "Machine Learning Methods for Longitudinal Data with Python," which is ta= king place online from 6-9 May. This course will provide a comprehensive in= troduction to analyzing sequence data (repeated over time or space) when ti=
me and causation play a crucial role.=0A =0AThis course will cover both cla= ssical statistical and modern machine learning approaches to handling time-= dependent data. Participants will learn how to recognize and address tempor=
al dependencies, disentangle cause-effect relationships, and apply appropri= ate modeling techniques for forecasting, survival analysis, and multi-omics=
data integration. Topics will include:=0AStatistical and machine learning = methods for sequence data=0ABias resolution: confounding, colliding, and me= diator biases=0ATime-series forecasting and predictive modeling=0ABayesian = networks and graph models=0AApplications in epidemiology, gene expression, = and multi-omics=0AThe course combines lectures, hands-on exercises, and cas=
e studies to ensure participants gain practical skills for applying these m= ethods to real-world biological data.=0A =0A =0ATo register or learn more, = please visit [ https://www.physalia-courses.org/courses-workshops/longitudi= nal-data/ ]( https://www.physalia-courses.org/courses-workshops/longitudina= l-data/ )=0A =0ABest regards,=0ACarlo=0A =0A =0A =0A=0A--------------------= =0A=0ACarlo Pecoraro, Ph.D=0A=0A=0APhysalia-courses DIRECTOR=0A=0Ainfo@phys= alia-courses.org=0A=0Amobile: +49 17645230846=0A=0A[ Bluesky ]( https://bsk= y.app/profile/physaliacourses.bsky.social ) [ Linkedin ]( https://www.linke= din.com/in/physalia-courses-a64418127/ )=0A=0A=0A

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