Intern
Lehrstuhl für Wirtschaftsinformatik und Künstliche Intelligenz im Unternehmen

Lehre

Die Lehre des Lehrstuhls beschäftigt sich mit Kompetenzen, wie sie in der Praxis von Data Scientists, Machine Learning Engineers und Entscheidern an der Schnittstelle Management und Informationstechnologie benötigt werden. Diese umfasst sowohl Fertigkeiten im Umgang mit Methoden und Verfahren der künstlichen Intelligenz, als auch in der Gestaltung vollständiger Datenanalysepipelines, inkl. Tools und IT-Infrastruktur. Darüber hinaus setzt der Lehrstuhl Schwerpunkte auf Methoden und Verfahren aus den Bereichen Smart Cities, Energie und Mobilität - beispielsweise auf Verfahren zur Analyse räumlicher Daten.

Die Philosophie der Lehre des Lehrstuhls ist es, Studierende in die Lage zu versetzen, komplexe Probleme eigenständig zu lösen. Dafür strebt der Lehrstuhl grundsätzlich eine formale Ausbildung an, um die Studierenden beim Aufbau wiederverwendbarer methodischer und anwendungsorientierter Kompetenzen zu unterstützen.

Welche Vorlesungen jeweils im Sommer- und Wintersemester angeboten werden entnehmen Sie bitte dem Menü. Des Weiteren bietet der Lehrstuhl verschiedene Seminare für Bachelor- und Masterstudenten an.

Wintersemester

Level: Bachelor

Scope: 4 SWS / 5 ECTS

Links: WueStudy

In this course, students will become proficient in geospatial data science. Students learn essential tools for data manipulation, spatial data handling, and more advanced spatial data analytics techniques like clustering and spatial modeling. Students will gain the skills to extract meaningful insights from real-world geographical data and use them to solve business problems. The course covers both theoretical concepts as well as the necessary application-oriented tools (using Python and Jupyter notebooks) to become a skilled geospatial analyst ready to make data-driven decisions.

Content

  • Spatial & non-spatial data manipulation in Python
  • Displaying spatial information and creating maps in Python
  • Exploratory Spatial Data Analysis
  • Spatial Feature Engineering
  • Models for Spatial Data Science

Organization

Links to infrastructure and all learning materials will be provided on WueCampus:

  • Jupyter Notebooks
  • Github classrooms & codespaces
  • Datasets and exercises
  • Solution templates

Examination: 

Students will need to solve small assignments throughout the semester. As final examination, students will work on a real-world geospatial data science problem and need to provide a written report including data & code-based evidence. 

Level: Master

Scope: 4 SWS / 5 ECTS

Links: WueStudy

In the course, students work on advanced data science projects. In this process, the entire data science workflow is followed, starting from data collection, through data preprocessing, to modeling, evaluation, and deployment. By using the top-down approach, students are empowered from the beginning to independently apply complex machine learning models.

Learning Objectives

  • Getting to know the principles and frameworks in the research field of Data Science,
    presentation of numerous application examples
  • Design, implementation and evaluation of the most important algorithms within an end-to-end workflow in the field of Data Science (including import of the data, their analysis and their processing)
  • Application of Jupyter notebooks and their infrastructure (collection, storage, retrieval and analysis of data)
  • Understanding of a data-based & analytical approach to decision-making problems
    Providing knowledge for the implementation and execution of solutions in the field of business analytics

Agenda

  1. Introduction
  2. Descriptive Analytics
  3. Recap: Machine Learning
  4. Feature Engineering
  5. Deep Learning for Tabular Data
  6. Deep Learning for Computer Vision
  7. Deep Learning for Image Segmentation and Spatial Data
  8. From Data to Production
  9. Deep Learning for Natural Language Processing
  10. Natural Language Processing with Huggingface
  11. Guest Lecture

Organization

Learning Materials on WueCampus:

  • Jupyter Notebooks
  • Datasets and exercises
  • Uploaded solution template

Exam: Final group project

Sommersemester

Level: Bachelor

Scope: Lecture (2 SWS) and exercise session (2 SWS) / 5 ECTS

Links: WueStudy

Learning Objectives

In this course, you will learn the concepts and terminology of simulation, as well as how to create and analyze simulation models using a simulation software package. You will also learn how to design and conduct simulation experiments, and how to validate and verify simulation models.

You will gain a solid foundation in probability and statistical analysis, which is essential for understanding and analyzing simulation results. You will also have the opportunity to explore a range of simulation applications in various fields, such as engineering, operations research, finance, and healthcare.

In addition, you will have the chance to apply these techniques to real-world problems through case studies.

Agenda

  • Introduction to simulation: concepts, terminology, and applications.
  • Probability and statistical analysis: probability distributions, random variables, statistical tests, and confidence intervals.
  • Design of experiments: principles of experimental design, design of simulation experiments, analyzing simulation results.
  • Validation and verification: techniques for validating and verifying simulation models, sensitivity analysis, and model robustness.
  • Applications of simulation: examples of simulation applications in various fields, such as engineering, operations research, finance, and healthcare.
  • Case studies: application of simulation techniques to real-world problems.
  • Simulation software: overview of simulation software packages and their features, hands-on experience with a simulation software package.

Organization

Learning Materials on WueCampus:

  • Lecture Slides
  • Datasets and exercises

Exam: Final written exam

Level: Master

Scope: Lecture (2 SWS) and exercise session (2 SWS) / 5 ECTS

Links: WueStudy

Learning Objectives

In this course, you will learn

  • the fundamentals for developing, deploying and maintaining machine learning systems in companies (MLOps),
  • to design the associated IT infrastructure,
  • to manage machine learning projects in organizations, including staffing and the choosing an appropiate organizational form.

You will refine and test your skills by practicing the theoretical concepts during exercise sessions. This practice will prepare you for the final project, where you and your peers will develop your own Machine Learning System.

Agenda

  • Introduction to Enterprise AI
  • Business Requirements for AI Systems
  • ML Ops I: Data Engineering
  • ML Ops II: Obtaining Training Data
  • ML Ops III: Data Preprocessing
  • ML Ops IV: Feature Engineering
  • ML Ops V: Modeling & Evaluation
  • ML Ops VI: Deployment
  • ML Ops VII: System Monitoring 
  • ML Ops VIII: Updating in Production
  • Guest Lecture
  • Instrastructure and Tools
  • Managing Machine Learning Teams

Organization

Learning Materials on WueCampus:

  • Lecture Slides
  • Datasets and exercises
  • Uploaded solution template

Exam: Final group project

Ganzjährig

Der Lehrstuhl für Künstliche Intelligenz im Unternehmen bietet ganzjährig Seminare und Abschlussarbeiten an. Im Bachelor findet das Seminar "Wirtschaftsinformatik und KI im Unternehmen" und im Master das Seminar "Business Analytics" statt. Sowohl in den Seminaren als auch in den Abschlussarbeiten werden aktuelle Themen des Lehrstuhls aus den Bereichen KI und Optimierung behandelt. Dabei legen wir besonders Wert auf die praktische Anwendung.

  • Die Anmeldung findet sowohl für Seminare als auch für Abschlussarbeiten über FLIP statt.

  • Anschließend erhalten Sie von uns eine Rückmeldung per E-Mail mit weiteren Informationen.

Darüber hinaus bieten wir in jedem Semester ein Projektseminar an, das in Kooperation mit einem Praxispartner durchgeführt wird.