Attention: the admission period for the selection minoren is still open until today 23:59hrs (15 April 2024).

Designing with advanced artificial intelligence


About this course

The course DBM180 explores the cutting-edge capabilities of machine learning and artificial intelligence for designing innovative systems and experiences. Students will gain a deep understanding of classical ML algorithms and techniques, including supervised, unsupervised learning, and more. They will learn how to apply these powerful methods to tackle complex real-world problems and create AI-powered designs that provide unique value propositions. Specifically, students will employ these methods to design prototypes for solving real-world problems, such as a system to classify products, and analyze movie reviews. The projects will showcase how to harness AI to gain value from diverse data sources. With these hands-on projects and exposure to powerful ML software frameworks (such as Weka), students will gain the knowledge and skills required to push the forefront of AI-powered design. It includes the following:

  • A generic architecture for designing intelligent systems comprising data repository, predictive engine, and managing potential feedback loops
  • Apply architectural blueprint to design systems (digital or physical) providing predictive response
  • Predictive engine uses data to predict optimal system responses to provide optimized experience, help automating workflow, or useful information to the human user
  • Collect and store user feedback to further fine-tune and improve the model
  • Successful prototypes could lead to further research or publications

Learning outcomes

  • Describe key machine learning algorithms and their implementation

  • Conceptualize a phenomenon as a learning problem and identify relevant data

  • Recognize core components of ML-enabled systems including outcomes, data, and user experience

  • Describe data collection strategies or recognize potential data sources for ML system including training, validation and test data acquisition

  • Implement conceived machine learning-powered systems for a given context and problem.

  • Distinguish key evaluation metrics to benchmark model performance in a given context

  • Develop concepts focused on user experience and prototype its key components in groups or individually culminating in a capstone project

  • Reflect individually on learning goals and group contributions

Required prior knowledge

You must meet the following requirements

Link to more information

If anything remains unclear, please check the FAQ of TU Eindhoven.


  • Start date

    11 november 2024

    • Ends
      19 januari 2025
    • Term *
      Block GS2
    • Location
    • Instruction language
    Enrolment starts in 60 days
These offerings are valid for students of Utrecht University