AI in Practice: Implementing Real-World Solutions

We are organizing this lecture together with Prof. Dr. Gerard de Melo and the chair of „Artificial Intelligence and Intelligent Systems“ in the fall semester of 2023/2024.

Target group: Students of the HPI Master's Programmes

Prof. Dr. Gerard de Melo (Chair of Artificial Intelligence and Intelligent Systems)
Gregor von Dulong (School of Entrepreneurship)
Michael Mansfeld (School of Entrepreneurship)

Goals of the lecture

  • Work on and solve practical and realistic problems in small teams 
  • Provide the partner companies with practical approaches to solving problems in the field of artificial intelligence


  • Start: Mid October 2023Duration: 3 Months
  • Workload for students: 1 day per week
  • After an introductory event for presenting the tasks and finding the teams, the students will work on the solution throughout the semester and then present it at a final presentation.
  • Accompanying events, coaching and feedback sessions will take place to support the solution of the tasks and enable further contact points.

Close, practical cooperation with partner companies

  • Introduce a Data Analysis/ML challenge with practical relevance
  • Participation in the introductory event to present the problem definition and in the final event to receive and evaluate the project results
  • Regular feedback sessions with the students (1-4 times per month)

Example topics for challenges

  • Exploratory data analysis of a company dataset (e.g., unstructured text data, machine or sensor data)
  • Development of a data analytics dashboard to monitor signals extracted from incoming data
  • Implementation of a machine learning model based on a suitable real-world dataset in order to tackle a current business / operational challenge
  • Automating a manual business process by prototyping an AI-driven approach (e.g., extracting relevant information from incoming documents)
  • Devising a data integration strategy to connect multiple existing internal data sources
  • Analysis of the current data landscape & identification of data engineering, analytics, and optimisation possibilities