Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introductory approaches with the latest advanced techniques. Beginning with an introduction of univariate and multivariate statistical analysis, the book then explores multivariate calibration and validation methods. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical chemistry are then discussed, providing useful examples of the techniques in chemistry applications.
Drawing on the knowledge of a global team of researchers, this book will be a helpful guide for chemists interested in developing their skills in multivariate data and error analysis.
- Provides an introductory overview of statistical methods for the analysis and interpretation of chemical data
- Discusses the use of machine learning for recognizing patterns in multidimensional chemical data
- Identifies common sources of multivariate errors
1. Statistical Methods in Chemical Data Analysis
2. Multivariate Predictive Modeling and Validation
3. Multivariate Pattern Recognition by Machine Learning Methods
4. Tuning the Apparent Thermodynamic Parameters of Chemical Systems
5. The Analytical/Measurement Sources of Multivariate Errors
6. Autoencoders in Analytical Chemistry
7. Uniqueness in Resolving Multivariate Chemical Data
Appendix
1. Introduction to Python
Dr. Jahan B Ghasemi received his PhD from Shiraz University. He was a visiting Scientist at the University of Chalmers in 2001 and Delaware University in 2006. His current research interests are focused on chemometrics and data analysis and computational drug design. He is the author of more than 200 papers and 4 chapter books in international journals and books.
