Content, progress and pedagogy of the module

Learning objectives

Knowledge

• must have knowledge about one- and two-dimensional signal/data representations
• must have knowledge about how sparse representations and/or statistical techniques influence real-world data/signals
• must have knowledge about at least one of the following aspects:

1. classical and Bayesian statistical methods for processing of noisy signals
2. simulation techniques and Markov chain and Monte Carlo methods
3. compressed signal/data representations

Skills

• Must be able to apply relevant theories, methods and techniques to ensure that at least one of the following requirements is satisfied:

1. is able to use Bayesian and/or hierarchical statistical methods to analyse time series and lattice data and to evaluate the validity of the results obtained
2. is 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

• must be able to communicate results of statistical analyses and/or sparse representation techniques to non-specialists within advanced signal processing

• must be able to develop statistical models and/or sparse representations suitable for analysis of real-world signals such as noisy digital images or communication signals

• must be 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

• must be able to communicate research-based knowledge and discuss the professional and scientific problems to peers inside and outside of mathematics and technology

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