Machine Learning specificities in Healthcare

Machine Learning specificities in Healthcare

Course Description

Machine Learning has achieved expert-level accomplishment over the last years: self-driving cars, face & voice recognition, Go & Chess playing, translation … Nevertheless, the tools and methods backing these breakthroughs have not shown similar momentum in Healthcare despite the considerable efforts of the scientific and industrial communities. Healthcare is an extremely challenging field for Machine Learning – thus full of scientific opportunities – as it wraps three ingredients: – Diverse mathematical applications: disease diagnosis, cell segmentation, prediction of age at disease onset, clustering of patients or symptoms, simulation, generation, etc – The incredibly large variety of data: biomarkers, imaging data, time-series, genomics, graphs, text, etc. – Case-specific constraints: small datasets, high dimensional 3D images, real-time usage, anonymization, false-positive importance, unbalanced datasets, etc. The course aims at first introducing the particularities that Machine & Deep Learning faces when dealing with medical applications. The second part of the lecture will focus on state-of-the-art models and new paradigms developed in the scientific community to tackle these challenges. In a nutshell, the lecture is suited to both neophytes interested in getting a general overview of Healthcare applications and also to advanced ML practitioners willing to expose themselves to medical challenges. Topics:
  • Medical data,
  • Medical imaging,
  • Deep Learning,
  • Disease Modelling,
  • Cross-sectional & longitudinal data

Course tools

  • Python


  •  Calculous & Algebras
  • At ease with standard ML algorithms & objects (tensors, distributions, loss, energy, …)
Level of complexity of course Intermidiate


Dr. Igor Koval Postdoctoral Researcher in Machine Learning [ Inria ] x [ Paris Brain Institute ] After an MSc in Data Science & MSc of Engineering, Igor defended his PhD dissertation in Machine Learning applied to neuroscience, with a particular focus on the progression of neurodegenerative diseases (Alzheimer, Parkinson, Huntington). His current scientific interests lie in the stratification of patients to better design clinical trials. He has a strong focus on the development of user-friendly tools for researchers and clinicians to deploy the methods into the clinics. He is also the Deep Learning lead instructor for a French data science bootcamp program. Fields of interests: Statistical Learning, Disease Modelling, Deep Learning, Longitudinal data Contacts: /

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