Prerequisite/Recommended prerequisite for
participation in the module
Solid knowledge in probability, statistics, linear algebra, Fourier
theory, and programming
Content, progress and pedagogy of the
module
Learning objectives
Knowledge
- Have knowledge about the theoretical framework in which
stochastic processes are defined
- Be able to understand the properties of the stochastic
processes introduced in the course, such as white-sense stationary
(WSS) processes, Auto Regressive Moving Average (ARMA) processes,
Markov models, and Poisson point processes
- Be able to understand how WSS process are transformed by
linear-invariant systems
- Be able to understand the theoretical context around the
introduced estimation and detection methods ((non-parametric and
parametric) spectral estimation, Linear Minimum Mean Square Error
(LMMSE) estimation, Wiener filter, Kalman filter, detection of
signals, ARMA estimation, etc.)
Skills
- Be able to apply the stochastic processes taught in the course
to model real random mechanisms occurring in engineering
problems.
- Be able to simulate stochastic processes using a standard
programming language.
- Be able to apply the taught estimation and detection methods to
solve engineering problems dealing with random mechanisms.
- Be able to evaluate the performance of the introduced
estimation and detection methods
Competences
- Have the appropriate “engineering” intuition of the basics
concepts and results related to stochastic processes that allow –
for a particular engineering problem involving randomness – to
design an appropriate model, derive solutions, assess the
performance of these solutions, and possibly modify the model, and
all subsequent analysis steps, if necessary.
Type of instruction
The programme is based on a combination of academic,
problem-oriented and interdisciplinary approaches and organised
based on the following work and evaluation methods that combine
skills and reflection:
- Lectures
- Classroom instruction
- Project work
- Workshops
- Exercises (individually and in groups)
- Teacher feedback
- Reflection
- Portfolio work
Extent and expected workload
Since it is a 5 ECTS course module, the work load is expected to
be 150 hours for the student
Exam
Exams