Event
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Event
2.01.378 Practical multimodal-multisensor data analysis pipelines -
Event date(s) | room
- Montag, 17.10.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 20.10.2022 10:15 - 11:45 | A04 4-407
- Montag, 24.10.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 27.10.2022 10:15 - 11:45 | A04 4-407
- Donnerstag, 3.11.2022 10:15 - 11:45 | A04 4-407
- Montag, 7.11.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 10.11.2022 10:15 - 11:45 | A04 4-407
- Montag, 14.11.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 17.11.2022 10:15 - 11:45 | A04 4-407
- Montag, 21.11.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 24.11.2022 10:15 - 11:45 | A04 4-407
- Montag, 28.11.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 1.12.2022 10:15 - 11:45 | A04 4-407
- Montag, 5.12.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 8.12.2022 10:15 - 11:45 | A04 4-407
- Montag, 12.12.2022 10:15 - 11:45 | V03 2-A208
- Donnerstag, 15.12.2022 10:15 - 11:45 | A04 4-407
- Montag, 9.1.2023 10:15 - 11:45 | V03 2-A208
- Donnerstag, 12.1.2023 10:15 - 11:45 | A04 4-407
- Montag, 16.1.2023 10:15 - 11:45 | V03 2-A208
- Donnerstag, 19.1.2023 10:15 - 11:45 | A04 4-407
- Montag, 23.1.2023 10:15 - 11:45 | V03 2-A208
- Donnerstag, 26.1.2023 10:15 - 11:45 | A04 4-407
- Donnerstag, 2.2.2023 10:15 - 11:45 | A04 4-407
Description
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.
Contact:
Thiago S. Gouvêa
Lecturers
SWS
4
Art der Lehre
Hybrid (Online und Präsenz)