Data science and the curse of phase transitions
published: Nov. 28, 2016, recorded: November 2016, views: 2303
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Extracting information, and more generally extracting knowledge from large datasets is arguably one of the main frontiers of modern science, common to a broad variety of disciplines. Bayesian approaches to machine learning and signal processing provide a conceptual framework in which information bits interact through constraints (due to prior knowledge or to measurements). Statistical physics has helped to develop new approaches and very powerful algorithms in this context, where collective phenomena, like phase transitions and the occurrence of glassy phases, play a major role. This talk will review some of the main developments in this field, illustrated by specific examples like compressed sensing.
Download slides: BIDSAconference2016_mezard_data_science_01.pdf (6.4 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !