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Semester: Wintersemester 2022

2.01.378 Practical multimodal-multisensor data analysis pipelines -  


  • Montag: 10:15 - 11:45, wöchentlich (17.10.2022 - 30.01.2023)
  • Donnerstag: 10:15 - 11:45, wöchentlich (20.10.2022 - 02.02.2023)


We know that multimodal-multisensor data is profoundly different from past data sources. It is extremely rich and dense data that typically involves multiple time-synchronized data streams, and it also can be analyzed at multiple levels such as signal, activity pattern, representational, transactional, etc. When multimodal-multisensor data are analysed at multiple levels, they constitute a vast multi-dimensional space for discovering important new phenomena with applied artificial intelligence methods (The Handbook of Multimodal-Multisensor Interfaces, Vol I, https://dl.acm.org/doi/book/10.1145/3015783).

This year's course focusses on Data Analysis Pipelines for Multivariate Time Series for Sustainability: Yearly greenhouse gas emissions of OECD countries, fluctuations on the population size of endangered species, sensor readings on a biochemical reactor: multivariate time series data are generated whenever someone monitors a phenomenon over time. Extracting knowledge from them is a process that starts with obtaining the data, iteratively visualising and transforming, and finally summarising the data into an interpretable representation – whether graphical or mathematical.
This course will cover good practices and practical aspects of all steps in the process – handling file input, organising a project’s code, transforming the data with spectral and machine learning methods, and generating models and visualisations that capture relevant structure in the data.

Thiago S. Gouvêa



Art der Lehre
Hybrid (Online und Präsenz)

(Stand: 10.08.2023)  |