What it is about
You will learn to systematically describe uncertainties in technical models and treat them quantitatively. In the end you can apply probability models to engineering questions, perform Monte Carlo simulations, and determine parameterized models from data using maximum likelihood and Bayesian estimation.
What you will be able to do
- Understand the fundamentals of probability theory
- Handling multiple random variables and Monte Carlo methods
- Parameter estimation: maximum likelihood and Bayesian estimation
- Probabilistic regression and classification (probabilistic machine learning)
- Basic concepts of decision making and design under uncertainty
What the module consists of
- LectureConveying the underlying theory and computational examples
- ExercisesDeepening theoretical knowledge and expanding the course content
- Demonstrations (Matlab)Application and visualization through computational examples
Teaching method
- LecturesExplain the theoretical foundations and cover computational examples
- ExercisesServe to deepen and consolidate what has been learned
- Demonstrations with MatlabShow practical applications and implementations