Machine Learning for Chemical Sciences and Industry

The course main goal is to introduce students in industrial chemistry and chemistry to the fundamentals of machine learning and some of its applications to chemical sciences and industrial chemistry.
The course will aim at presenting:
- the topic of supervised machine learning through description of data sampling for training and test, with illustration and discussion of different models (linear, kernels, regression, neural networks);
- applications of supervised machine learning to the construction of potential energy surfaces for chemical spectroscopy, kinetics, and thermochemistry;
- unsupervised machine learning with topics of interest in industrial chemistry (flux maps, control charts), and chemical sciences (principal component analysis);
- the possibility to exploit the numerous instrumentation and control data acquired in the chemical industry to improve diagnostics of problems, quality prediction, and control optimization.

At the end of the class, the students will be able to:
1. illustrate the basic concepts of machine learning;
2. understand the theoretical and practical difference between supervised and unsupervised models;
3. proficiently discuss the theoretical foundations and practical aspects of different learning models;
4. appreciate the importance of machine learning for chemical sciences in applications involving spectroscopy, kinetics, and thermochemistry;
5. appreciate the importance of machine learning to improve industrial and control processes, advance quality prediction, enhance environmental sustainability and optimize energy consumption in the chemical industry.