The guest of this episode is Jean-Marc Mercier. Dr. Jean-Marc studies machine learning, both kernel methods and deep learning, in the context of mathematical finance.
We start talking about the differences between kernel methods and deep learning and some history of machine learning, then about the relations between orthogonal polynomials, and deep learning and kernel methods, touching on the application of kernel principal component analysis in aerospace and optimal transport. Dealing with finance, we talk about his vision in AI algorithmic trading and in general more financial applications where AI can be useful. Then we move on modelling approach and assumptions of the observable that brought us to economic bubble formation. We reserve quite a lot of time to talk about "codpy" an open-source python library for machine learning, mathematical finance and statistics of which Jean-Marc is one of the authors. We end up speaking about "codpy" more in detail such as function representation, mesh free methods which bring us to its applicability in fluid dynamics and we conclude with the future expansions of this library.

LINKS:
https://www.researchgate.net/profile/Jean-Marc-Mercier
https://pypi.org/project/codpy/

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