Injecting Machine Learning Everywhere
Saturday, March 12
9:30AM - 10:30AM
Austin Convention Center
531 E 4th St
Machine learning could be useful to your project, but you might not know it yet. Programming, science, engineering, and even art: all could benefit from increased use of this powerful method. With the advent of automatic machine learning tools, this technique is becoming more accessible and popular. In this talk, I will show applications of machine learning using real-world examples from a wide-range of domains. I will demonstrate how you could use recently developed tools to easily create a machine-learned task, and embed it in your project. Finally, I will discuss cutting-edge applications of machine learning and the future of this field.
Etienne Bernard holds a PhD in statistical physics from ENS Paris. During in thesis "Algorithms and applications of the Monte Carlo method: two-dimensional melting and perfect sampling", he designe...Show the rest
Etienne Bernard holds a PhD in statistical physics from ENS Paris. During in thesis "Algorithms and applications of the Monte Carlo method: two-dimensional melting and perfect sampling", he designed Markov-chain Monte Carlo algorithms in order to solve physics problems. He then worked as a postdoctoral scholar at MIT on problems related to Monte Carlo algorithms and non-equilibrium statistical physics. Etienne is now working in the Advanced Research Group of Wolfram Research, developing machine learning functionalities for the Wolfram Language. He developed "Classify" and "Predict", which are highly automated functions to perform supervised learning. Etienne's work aims to simplify the practice of machine learning in order to spread its utilisation.Hide the rest