Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:
- 12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
- 9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
- 6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.
In the module description (download pdf) you find the entire language information per module divided into the following categories:
Many data sets are temporal by nature.
The first part of the course presents techniques for analysis of time series. It starts from visualization techniques; then it shows techniques for characterizing trend and seasonality; eventually it present structured statistical approaches based on exponential smoothing and arima techniques. Several examples referring to real data sets are shown.
In the second part of the course students learn how to analyze digital signals in different domains, i.e. time and spectral domain; they learn how to extract meaningful features from digital signals suitable for classification. Finally, they learn how to set up and learn statistical models, such as HMMs or DNNs, for recognizing and classifying time series.
The course adopts a practical approach: theoretical concepts are illustrated and applied in specific case studies.
A probabilistic approach is emphasized throughout the course.
The labs are done using environments for scientific programming such as R or Matlab or Python.
Basic knowledge in statistics.
Programming with scripting languages.
- Students know how to visualize time series and how to characterize their main features.
- Students know how to evaluate forecast accuracy.
- Students know how to model trends, seasonalities and non-stationarities adopting exponential smoothing and ARIMA models.
- Students know how to perform model estimation, model selection and probabilistic prediction with these models.
- Students know different methods to analyse digital signals in different domains
- Students know how to extract important features used in speech processing
- Students learn to apply Bayes rule for classifying digital signals.
- Students can apply modern deep learning approaches to classify digital signals
Contents of Module
Part 1: Forecasting sequential data
- Time series graphics.
- Main features of time series.
- Assessment of the predictions.
- Exponential smoothing
- ARIMA models
Practical case studies.
Part 2: Analysis and classification of digital signals
- Analysis of digital signals in different domains
- Feature extraction
- Modelling, classification & recognition of digital signals
- Classic Approaches: Dynamic Time Warping, Vector Quantization
- Statistical modelling: Hidden Markov Models
- Deep Learning Approaches
Practical case studies.
Teaching and Learning Methods
- Ex cathedra
- Self study
- Practical exercises with computer
- Graded homeworks / project.
Slides will be available covering the topics of the course.
In addition, recommended books are:
R. Hyndman and G. Athanasopoulos., Forecasting: Principles and Practice, Springer, 2018 (online free textbook at https://otexts.org/fpp2/)
For digital signal processing:
X. Huang, A. Acero, H.-W. Hon: Spoken Language Processing, Prentice Hall, 2001, ISBN 0-13-22616-5
L. R. Rabiner und B.-H. Juang, Fundamentals of Speech Recognition. Prentice Hall, 1993.
D. Yu und L. Deng, Automatic Speech Recognition: A Deep Learning Approach. Springer London, 2014.