6 credits upon successful participation in the exam.

• Introduction to measurement error analysis and the law of error propagation
• Statistical data analysis methods: linear and nonlinear correlations, principal component analysis, etc.
• Methods of linear and nonlinear optimisation
• Adaptation of linear and nonlinear, univariate and multivariate model functions to measured data
• Classical and Bayesian methods for determination of the error intervals of estimated model parameters and their mutual dependencies
• Relations between nonlinear model adaptation and supervised machine learning
• Unsupervised learning ("clustering") methods and their application to measured data

• Press, W. H., et al. (2007). Numerical Recipes: The Art of Scientific Computing. 3rd edition, Cambridge University Press.
• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer-Verlag.
• Gelman, A., et al. (2013). Bayesian Data Analysis. CRC Press.
• Rengaswamy, R., & Suresh, R. (2022). Data Science for Engineers (1st ed.). CRC Press. https://doi.org/10.1201/b23276

In this module, the participants will acquire the competency of interpretating of measured data critically and of distinguishing significant from insignificant model parameter estimation. Furthermore, they will gain a deepended understanding of supervised machine learning methods in the context of nonlinear model adaptation. Furthermore, participants will learn to understand and apply unsupervised machine learning methods for the analysis and visualisation of high-dimensional measured data.