Content, progress and pedagogy of the module

Learning objectives

Knowledge

• must have knowledge about compression of one and two dimensional signal/data representations
• must have knowledge about classical and Baysian statistical methods for processing of noisy signals
• must have knowledge about simulation techniques and in particular about Markov chain and Monte Carlo methods
• must have knowledge about how sparse representations and statistical techniques influences real-world data/signals

Skills

• must be able to use Baysian and hierarchical statistical methods to analyse time series and lattice data and to evaluate the validity of the results obtained
• must be able to use compressed signal/data representations on real or synthetic data and be able to evaluate the quality of the signal/data reconstruction

Competences

• are able to communicate results of statistical analyses to non-specialists within advanced signal processing
• are able to independently develop statistical models suitable for analysis of real-world signals such as noisy digital images or communication signals
• are able to use sparse representations and/or statistical methods to solve a given practical problem and, if needed, make minor adjustments to the methods to obtain the wanted functionality

Type of instruction

Project work.

Extent and expected workload

This is a 15 ECTS project module and the work load is expected to be 450 hours for the student.
 

Exam

Exams