Hasso-Plattner-Institut
  
 

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 2021/2022.

Target group: Students of the HPI Master's Programmes

Lecturers:
Prof. Dr. Gerard de Melo (Chair of Artificial Intelligence and Intelligent Systems)
Tolga Buz (HPI E-School)

 

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
     

Procedure/Organisation

  • Start: October 2021Duration: 3 Months
  • Workload for students: 1-2 days 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