Stud.IP Uni Oldenburg
University of Oldenburg
26.02.2024 00:55:02
Lecture: 2.01.378 Practical multimodal-multisensor data analysis pipelines - Details
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General information

Course name Lecture: 2.01.378 Practical multimodal-multisensor data analysis pipelines
Subtitle inf378
Course number 2.01.378
Semester WiSe22/23
Current number of participants 8
expected number of participants 12
Home institute Department of Computing Science
Courses type Lecture in category Teaching
First date Monday, 17.10.2022 10:15 - 11:45, Room: V03 2-A208
Type/Form Lecture + Exercises
Pre-requisites Basic familiarity with Python and machine learning concepts
Lehrsprache deutsch

Rooms and times

V03 2-A208
Monday: 10:15 - 11:45, weekly (11x)
A04 4-407
Thursday: 10:15 - 11:45, weekly (13x)


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,

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

Admission settings

The course is part of admission "Anmeldung gesperrt (global)".
Erzeugt durch den Stud.IP-Support
The following rules apply for the admission:
  • Admission locked.