Machine Learning in Logistics

Lecturer Prof. Dr.-Ing. Carlos Jahn
Contact person Marvin Kastner
Target Audience LIM / IWI
Lecture type Lecture & Exercise
  Name of lecture Digitalization in Traffic and Logistics
  Name of exercise Machine Learning in Logistics
  In cooperation with Institute for Software Systems
Lecture term Winter term
Language German
Credit Points 6 ECTS
Type of examination Exam (partially on laptop)


The topic of the lecture is the quantitative analysis of various types of data occurring in the field of transport and logistics. The focus will be on problems of maritime logistics. The students should be enabled to evaluate the learned methods with regard to their usability in concrete company contexts and to know and derive requirements and potentials of an effective application; for example related to data mining approaches for controlling or forecasting approaches for the operational planning of companies.

Within the course "Machine Learning in Logistics", students are introduced to a selection of methods of machine learning in the first half of the term. The lecture is called "Fundamentals of Machine Learning" and is organized by the Institute for Software Systems. In the second half of the term, the lecture "Digitalization in Traffic and Logistics" applies those methods to logistics-specific research questions. The course is accompanied by the exercise "Machine Learning in Logistics". Here, Jupyter notebooks are used to equip students with the optimal tools for data exploration.

Lecture contents

  • Machine Learning in science and industry
  • Time series data in traffic
  • Movement data
  • Image recognition and feature engineering
  • Anomaly detection


  • Aggarwal, Charu C. (2017). Outlier Analysis. Springer International Publishing Switzerland: Cham [Abstract] [www]

  • Chapman, Peter and Clinton, Janet and Kerber, Randy and Khabaza, Tom and Reinartz, Thomas and Russel H. Shearer, C and Wirth, Robert (2000). CRISP-DM 1.0 : Step-by-step data mining guide. [www]

  • Géron, Aurélien (2018). Praxiseinstieg Machine Learning mit Scikit-Learn und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. O'Reilly: Heidelberg [www]

  • Haneke, Uwe and Trahasch, Stephan and Zimmer, Michael and Felden, Carsten (2019). Data Science - Grundlagen, Architekturen und Anwendungen. dpunkt.verlag: Heidelberg [www]

  • Lenzen, Manuela (2020). Künstliche Intelligenz: Fakten, Chancen, Risiken. C.H. Beck: München [www]

  • VanderPlas, Jake (2017). Data Science mit Python : das Handbuch für den Einsatz von IPython, Jupyter, NumPy, Pandas, Matplotlib, Scikit-Learn. MITP: Frechen [www]