Institute ofMaritime Logistics

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MaLiTuP - Machine Learning in Theory und Practice

The project aims at developping and permanently establishing a qualification program called "Machine Learning in Theory und Practice" in order to teach the fundamentals of the field of machine learning to Master's students in logistics at TUHH.

Period
01.11.2017 - 30.04.2020
Project Funding
Funded by the Federal Ministry of Education and Research (BMBF)
Our Status
Partner
Contact Person
Marvin Kastner
PartnersMembers
  • TUHH, Institut for Software Systems
    Prof. Dr. Sibylle Schupp
  • Fraunhofer-Center for Maritime Logistics and Services
    Prof. Dr.-Ing. Carlos Jahn
  • TUHH, Institut of Maritime Logistics
    Prof. Dr.-Ing. Carlos Jahn
Partners
  • TRENZ AG
    The internationally active software company has been developing individual solutions since 1999. In the maritime sector, the company provides real-time information services for the maritime industry.
  • JAKOTA Cruise Systems GmbH / FleetMon
    The company has been working in the field of real-time vessel tracking since 2010 and uses 300 million position reports and weather data for analyses every day as a data basis.
  • Deutscher Wetterdienst
    The maritime weather centre of the German Meteorological Service performs tasks to increase the safety of maritime shipping. Meteorological predictions are made with the help of numerical prediction systems.

Description

Especially in the field of logistics, digitization is becoming more and more important, resulting in an ever increasing need for trained personnel in the field of machine learning. In addition to a class, practical projects are to be offered within the qualification program, which enable the students to apply the acquired knowledge in concrete and realistic case studies from the maritime world.

The methodological and content-related focus is on dealing with large amounts of data, their classification and correlation as well as the handling of data uncertainties. Furthermore, a further offer is aimed at university graduates with professional experience in the field of data analysis. In addition, the "MaLiTuP" research project is intended to enable the project partners to expand their own competencies in the field of big data analyses and forecasts.



Publications (excerpt)

[182401]
Title: Teaching Machine Learning and Data Literacy to Students of Logistics using Jupyter Notebooks [DELFI Poster Award Winner]. <em>DELFI 2020</em>
Written by: Kastner, Marvin and Franzkeit, Janna and Lainé, Anna
in: <em>DELFI 2020</em>. (2020).
Volume: Number:
on pages: 365-366
Chapter:
Editor: In Zender, Raphael and Ifenthaler, Dirk and Leonhardt, Thiemo and Schumacher, Clara (Eds.)
Publisher: Gesellschaft für Informatik e.V.:
Series: Lecture Notes in Informatics (LNI) - Proceedings
Address: Bonn
Edition:
ISBN: 978-3-88579-702-9
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: https://api.ltb.io/show/BMRWS
ARXIVID:
PMID:

[pdf] [www]

Note: malitup

Abstract: Teaching machine learning in fields outside of computer sciences can be challenging when the students do not have a solid code knowledge. In this work, the requirements for teaching data literacy and code literacy to students of logistics are explored. Specifically, the use of Jupyter Notebooks in a machine learning course for students in logistics is evaluated, using “Teaching and Learning with Jupyter” written by Barba et al. in 2019 that lists several teaching patterns for Jupyter Notebooks

BMBF