As you hopefully know by now, Montreal Python 6 is this Tuesday at la Banque. Here is our schedule for the evening:

  • 18h00: Opening
  • 18h45: Announcements and flash presentations
  • 19h15: Break
  • 19h45: Main presentation: Francis Piéraut on Machine Learning empowered by Python

Flash presenters will be:

  • Nathanaël Lécaudé: PyMT: module for developing multi-touch enabled applications
  • Stéphane Jolicoeur How Python is used at the National Film Board of Canada
  • Alexandre Bourget SFL Vault: a secure distributed crediential storage system
  • Herald Gjura The challenge of managing a horde of Pythonistas
  • François Pinard Tweeting with Python
  • Olivier Bélanger Ounk: a Python audio scripting environment for Csound; pitch for Montréal Python 7

In case you missed the original announcement, here is the abstract for the main presentation:

Machine Learning is a subfield of AI that considers learning patterns from existing data. Related applications are increasing in many fields where adaptive systems are needed, like fraud detection, face recognition, recommendation systems, disambiguation systems, insurance risk estimation, web traffic filtering, voice recognition, and many others. The first part of this presentation will cover the basics of machine learning; in the second part, we will dive into a real example and see the complete process of using machine learning to create a real-time digit recognition system using Mlboost, a python library. The practical approach should allow the audience to assimilate the most important concepts of machine learning and the critical need for data preprocessing. After a Software Engineer degree, Francis Piéraut made a research master in Machine Learning at LISA. During his research work, he developed flayers, a powerful C++ neural network library. During the beginning of his career, his spend several years in Montreal startups companies applying Machine Learning and statistical AI related solutions. In 2005, he released the first version of MLboost, a python library that allows him to speedup his Machine Learning projects by simplifying data preprocessing, features selection and data visualization.

See you there.

--Yannick Gingras