Machine Learning and Data Processing (2500036) – Course 2025/26 PDF
Contents
Elements in a decision making scheme: Decision Maker, Actions, Random States, Utility, Optimization Criterion. A priori schemes. A posteriori schemes. Probabilistic description of an experiment. Elements of supervised learning, unsupervised learning and reinforced learning.
Dedication
2h Large group + 2h Medium group + 5h 36m Self StudyPrincipal component analysis Principal Component Analysis Principal component analysis
Dedication
4h Large group + 4h Medium group + 2h Laboratory classes + 14h Self StudyBayesian model update. Prior and posterior.
Dedication
2h Large group + 2h Laboratory classes + 5h 36m Self StudyLeast squares, error functions for regression, probabilistic approach, sum of squares error as maximum likelihood, model selection, the curse of dimensionality, generalized regression. Linear models for regression Discriminant functions, connection to maximum likelihood. Model selection. Bayesian logistic regression. linear classification models
Dedication
4h Large group + 4h Laboratory classes + 11h 12m Self StudyBasic concepts of ANN The multilayer percetron Network training Regularization in ANN
Dedication
4h Large group + 8h Laboratory classes + 16h 47m Self StudyMonte-Carlo Method and Stochastic Finite elements Assignment of Stochastic finite elements
Dedication
1h Large group + 2h Laboratory classes + 4h 11m Self StudyDedication
4h Laboratory classes + 5h 36m Self Study