Register to the webinar :
Take on the challenge of modeling complex spectroscopic data using Machine Learning
On May 30, 2024 at 11 am (CEST), Astrid Maléchaux will present the most common advanced machine learning methods. During this 45-minute webinar, you will discover ML’s methods to go further in the analysis of your complex spectroscopic data.
These methods are usually used to:
- deal with non-linear relationships
- analyze heterogeneous databases
- predict complex parameters
- take into account variability factors
- and all this even with few samples!
Program of the webinar:
- What do we call complex data?
- Overview of Machine Learning Methods
- Local Regression (LWR)
- Support Vector Machines (SVM)
- Artificial neural networks (ANN)
- Decision trees and ensemble methods (CART and RF / XGBoot)
- Comparison between methods on a practical case
- Conclusion
- Questions & answers
Astrid Maléchaux,
Data Scentist Ondalys
For each Machine Learning method, the optimization of the model as well as the advantages and disadvantages will be detailed. They will be illustrated by a practical case and a comparison with PLS (Least Squares Regression Method).
With a strong experience in Chemometrics and Machine Learning, Astrid Maléchaux will share her expertise with you.
The Ondalys team supports its industrial clients in processing their complex data. Whether it is instrumental data (spectroscopic data, hyperspectral images, etc.), physico-chemical data (chromatography data, OMICS, etc.) or process data, Ondalys supports you to optimize their analysis and extract all relevant information.
