This course is an introduction to machine learning applied to chemistry. You will learn machine learning fundamentals, from basic linear regression to deep learning with neural networks. Machine learning is a very broad topic, and this course will not be an exhaustive review of all existing methods. Instead, specific focus will be given on how, in practice, to set up machine learning models using Python. This will be illustrated on concrete examples in chemistry. The course consists in four lectures organized as follows:

1) Introduction: machine learning fundamentals

2) Machine learning for chemistry: hands-on training on concrete examples

3) Deep learning for chemistry

4) And now, play on! mini-project     

Photochemistry is the branch of chemistry concerned with the chemical effects of light. We

Within the framework of supramolecular chemistry or polymeric science,  the general content will cover from the basics  to the advanced trends of (hybrid) systems  will be the following :


  • Photochromism- state of arte - Stéphane Aloïse - Lille University (3H)
  • Smart supramolecular polymer materials - R. Hoogenboom  Gand university (3H) (May you include an example of photochromic system? it would be fantastic )
  • Novel Photochromic  systems in supramolecular interaction with polyoxometalate- Chris Ritchie - Monash University (3H) (Chris, I guess you can iclude the polymers!)

The XAS lesson will not be done in classroom. Instead, videos are available online on moodle : Faculté des Sciences et Technologies (FST)/Département Chimie/Master/M2-MASTER IRACM/XAS

This course is divided in 4 part:

1-Introduction

2- XAS : XANES

3- XAS : EXAFS 1

4- XAS : EXAFS 2

Progress through this online module watching to the video in the order they are displayed.

At the end of the module you have to do a final test.  Date and time of the final test will be specified later.

The password to play videos is : ASCsynchrotron

Asma Tougerti