The new minor offering from Leiden, Delft and Erasmus will be visible in early March.

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

  • Code
  • Credits
    ECTS 5
  • Level
  • Contact coordinator
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There are currently no offerings available for students of Utrecht University