AI for chemistry

This course positions itself in the following of the Chemistry and Data Sciences lectures (Semester 1 -- M1). It aims to familiarize students with the fundamental principles of artificial intelligence (AI) and its specific application to chemistry. It explores basic AI concepts, focusing on machine learning (supervised and unsupervised) (lectures 6h). All the concepts covered will be illustrated through practical sessions primarily applying to various fields of chemistry (practical sessions 18h). The program includes:

- Introduction to AI, Data Types and their Representation
- Supervised AI Methods (Regressions, Random Forest, SVM…)
- Unsupervised Learning and Clustering (k-means)
- Neural Networks and Deep Learning (dense to convolutional, image analysis…)
- Graph Neural Networks (GNN): Describe a molecule as a social network!
- Interpretability, Robustness and Ethics. 

M1S1 Chemistry and data science