PSL Intensive Week


Computer vision and time series analysis for Physics and Engineering

As part of the special “transverse program” of PSL, we organize with the help of PSL a special 1 week course on the topic of “Computer vision and time series analysis for Physics and Engineering”. This course is open in priority to Master 2 students of PSL. It is also open to other students (Master and PhD) and researchers, subject to availability.

Important: Master students should check with their master’s administration if this course can be used to validate one of their master course.

Dates and location

Dates 28th March - 1st April, 2022

Location Parisanté Campus, 10 Rue d’Oradour-sur-Glane, 92130 Issy-les-Moulineaux

Pre-register to the Courses

Pre-registration is free but mandatory. Everyone will have to organize their own lunch. There are restaurants and food stores within walking distance of the campus. Both on-site and remote work will be offered, but teachers will only answer to on-site questions. Registration for on site work is limited to 25 listeners. PSL students have priority if they pre-register before January 31st.

Courses Content

This week-long course will be split into two types of classes: theory and practice.

Schedule

Day 1: 28/03, Morning, 9h-12h15: Introduction and two parallel sessions

  • Machine learning and sklearn, Arturo Amor or
  • Numerical modeling, Elie Hachem

Day 1: 28/03, Afternoon, 14h-17h15 : Parallel sessions

  • Machine learning and sklearn in practice, Arturo Baldo or
  • Numerical modeling lab sessions, Kathrin Smetana and David Ryckelynck

Day 2: 29/03, Morning: Deep classifiers of digital twins David Ryckelynck

Day 2: Afternoon: Auto-encoders for model reduction David Ryckelynck

Day 3: 30/03, Morning: Computer Vision Ivan Laptev

Day 3: Afternoon: Computer Vision, lab sessions Ivan Laptev and Antoine Yang

_Day 4: 31/03, Morning: Deep learning models for time series _ Matthieu Labeau

Day 4: Afternoon: Deep learning models for time series, lab sessions Matthieu Labeau

Day 5: 01/04, Morning: Multi-scale modeling of materials Felix Fritzen

Day 5: Afternoon: Reinforcement learning in engineering Elie Hachem