[Conference] Basics of Machine Learning for experimental sciences

International conferences for knowledge development

Machine Learning is a technology that has become essential in recent years. It is an artificial intelligence technology that analyses and interprets patterns and data structures to enable learning, reasoning and decision making without human interaction. This method allows predictions to be made from accurate data and thus facilitates decision-making to improve performance over time.

In order to understand this new method in the field of science, the Graduate School of Chemistry organised its first annual conference on the theme of “Machine Learning in Experimental Sciences” in partnership with the DiiP (Data Intelligence Institute of Paris).

Through two days of conferences and discussions, the participants were able to grasp the positive innovations of this new method

• On 12 July, the objective was to provide an overview of the Machine Learning methods developed and their use in the main fields of experimental research such as biology, physics, health, the environment, etc. The aim was to present the tools and their uses for the analysis of data classically encountered in science (image analysis, statistical data, etc.).
• On 13 July, the Graduate School of Chemistry organised a workshop on the theme: “Practical applications of machine learning in chemistry: prospects and pitfalls”. The aim was to establish the state of the art of machine learning in the main fields of chemistry (molecular chemistry, theoretical chemistry, atmospheric chemistry, materials and nanomaterials).