Machine Learning in Healthcare

Machine Learning in Healthcare

Course Description

Self-driving cars, face & voice recognition, Go & Chess playing, language translation, and stock-market predictions are just a few among many examples of the already existing accomplishment that Machine Learning achieved at an expert level. 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.

Course tools

  • None


  • Beginner knowledge of Machine Learning concepts
  • Introductory knowledge of Deep learning would be a plus 😉
Level of complexity of course Intermediate


Igor Koval Inria Post-doctoral fellow, Ph.D. in Applied Mathematics at the Paris Brain Institute, Deep Learning lead Instructor at Le Wagon  Education curriculum with a Licence in Applied Economics, a master of Engineering at Ecole Nationale des Ponts et Chaussées, master of Data Science at Ecole Polytechnique. Find more about me at Fields of interests: Deep Learning, Machine Learning, Statistical Learning, Computer Science, Programming

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