Many personalities. Two campuses. One UAS_BO.


We see the topic of artificial intelligence and data science as one of the topics of the future with a cross-sectional impact on many disciplines. Therefore, it is important to create structures here, among other things, in the master's area as well as in the transition to doctorates. This is where the Interdisciplinary Institute "Applied Artificial Intelligence and Data Science Ruhr" of the University of Applied Sciences Bochum would like to start and make a contribution from the extended Ruhr region for the academic master education and for the innovative companies in NRW. A special feature of the institute is its broad, interdisciplinary character. Almost all departments of the university are involved. The analysis of data and its utilization goes far beyond the inner circle of computer science and statistics. The fields of application such as Intelligent Mobility, Industry 4.0, geo-data analysis, robotics, SmartWater sensor networks and the adaptation of economic models and process models make this area of digital transformation one of the defining interdisciplinary tasks.

AI & Data Science Modules

At the various departments and locations, there is an exciting and extensive range of modules related to AI & Data Science, which is available for Master's students at BO or the Ruhr Master School. We focus here on the Master's offer, although there are also many excellent opportunities in the Bachelor's.

Members of AKIS contribute to the following modules:

Machine Learning and Data Mining

The module covers the basics and techniques of supervised and unsupervised machine learning. Among others, NumPy, sklearn and Keras are used.

Data Science

The module provides an overview of data-driven decision making using machine learning methods. Basics of Python, data preprocessing as well as modeling are introduced and applied in practical exercises using sample data.

Applied AI and Machine Learning

The module covers (Deep) Reinforcement Learning and Evolutionary Learning. Among others, Python, Keras and OpenAI Gym are used.

Smart Robotics

In the module, topics of probability, planning, decision making, supervised and unsupervised machine learning, and optimal control are covered with reference to mobile robotics.

Introduction to AI

Among other things, the module addresses the question "What is AI?". Further components are: Introduction Python and Keras, (Un-)Supervised Learning, Reinforcement Learning, GAN and Machine Learning Basics.

  • Professor: Prof. Dr.-Ing. Stefan Müller-Schneiders
  • Bachlor or Master: Bachelor
  • Study fields: Computer Science
  • Open for optional module: No
  • Previous Knowledge: Programming, mathematics 1 and mathematics 2
  • Link to degree program:

Big Data

Skills for handling large and unstructured data sets and their efficient analysis will be learned. For this purpose, data from real sensors, mobile devices and from open-data sources, and also taking into account ethical and legal aspects, will be used. Big Data application scenarios and processing techniques, infrastructures and ecosystems for big data analysis (including MapReducc techniques and Apache Hadoop) will be demonstrated. Fundamentals of NOSQL database systems as well as modern concepts of distributed data management will be discussed. Exploratory and structuring analysis methods, including data visualization and machine learning-based techniques, will be applied.

Computer Vision for Autonomous Driving

The module explores the introduction to autonomous driving (Level), sensors / sensor data fusion, computer vision and introduction to Carla.

Context-aware and Mobile Computing

Context-aware Computing can be understood both as a sub-discipline and as an application domain of AI, and is primarily concerned with the design and realization of intelligent assistants that automatically perceive, interpret, and appropriately respond to contexts and context changes.

Ruhr Turtlebot Competition

The module focuses on simultaneous localization and mapping with a ROS-based mobile robot.

Water Quantity Management and Hydrometry

In the module, additional to the basics of quantitative Water Management, competences for the analysis of Water Management data are given. Matlab and R are used.

Digitalization in the Industrial Environment

In the module, the different aspects of digitization in industrial practice are examined - from the software used (ERP, MES, CAD/ CAM) to the analysis of data (Exploartive Visualization, Machine Learning, AI) to concrete practical applications in the production and logistics environment.

Production & Logistics Management

In the module, current challenges and approaches to solutions in the areas of production and logistics are developed. One focus is on the area of digitalization.

For these modules, various shared teaching materials and documents are available on the Resources page. In addition, participants in these courses are actively invited to or help shape the other Events.


AI Advanced Training

Kurangebot auf blauem Hintergrund zum Thema Weiterbildung AI - Praxisnahe Weiterbildungskurse für maschinelles Lernen und künstliche Intelligenz

Machine Learning is becoming increasingly important in today's world. Time and again, there are innovations that are only possible with methods from the field of artificial intelligence and machine learning. The fields of application are very diverse:

  • Industry 4.0 / Smartfactory
  • Logistics and supply chain management
  • Predictive Maintenance
  • Customer analysis

You too can benefit from the new possibilities of modeling data. Bochum University of Applied Sciences offers webinars on machine learning as part of WeAI, as well as classroom training at your company. You will get an introduction to Python and learn about the relevant methods from decision trees to Convolutional Neural Networks. For more information, please visit the project website

PhD Opportunities

coming soon...



Prof. Dr. Jörg Frochte (Institute Director)
University of Applied Sciences Bochum
Campus Velbert/Heiligenhaus - Room 2.43
AG Applied Computer Science
Kettwiger Str. 20
42579 Heiligenhaus

E-Mail: joerg.frochte (at)
Phone: +49 (0)2056 5848-16711