Stud.IP Uni Oldenburg
University of Oldenburg
26.02.2024 00:55:02
Lecture: 2.01.378 Practical multimodal-multisensor data analysis pipelines - Details
You are not logged into Stud.IP.

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)

Comment/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
thiago.gouvea@uol.de

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.