Arnaud PAVON

USEEM - Unité de Simulation en Santé

Praticien hospitalier en Réanimation Médicale. MD in medical ICU

https://sefca-umdpcs.u-bourgogne.fr/nos-formations/pole-formation-e-sante-et-intelligence-artificielle/intelligence-artificielle-et-sante.html

Objectifs: - Proposer une base commune de formation des formateurs francophones à l’enseignement de la médecine par la Simulation. - Mettre en commun les compétences existant au niveau des Structures de Simulation en Santé pour les enseignants mais aussi pour les étudiants par la création d’un rés

By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application.

Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Ana

Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model