Digitalisation, Data Science and Machine Learning in Water Engineering (250MAG001) – Course 2025/26 PDF
Contents
Data sets and basic concepts of probability and statistics. Functional approximation and linear regression. Overdetermined linear systems, eigenvalues and singular values
Dedication
6h Large group + 5h Self StudyLarge data sets, redundancy and optimal representation. Features that describe the phenomenon optimally. Linear dimensionality reduction, PCA. Nonlinear dimensionality reduction. Stochastic field reduction, Karhunen-Loève Applications to water engineering
Dedication
13h Large group + 25h Self StudyAdvanced Linear Regression with Statistics Gaussian Regression: Kriging Neural Network Approximation Applications to Water Engineering
Dedication
13h Large group + 25h Self StudyUnsupervised Learning: k-means clustering Hierarchical Supervised Learning: Dendogram Supervised Learning and Linear Discriminants Support Vector Machines (SVM) Classification Trees and Random Forests Clustering: k-means, dendogram… Logistic Regression Applications in Water Engineering
Dedication
13h Large group + 25h Self Study