Designing with advanced artificial intelligence

DBM180

Over deze cursus

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

Leerresultaten

  • 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

Voorkennis

Je moet voldoen aan de volgende eisen

Bronnen

  • The Canvas website users a diversity of supporting materials (such as Weka book on machine learning, example Python notebooks, information on human-AI interfaces, etc.)

Aanvullende informatie

  • Studiepunten
    ECTS 5
  • Niveau
    master
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Aanbod

  • Startdatum

    11 november 2024

    • Einddatum
      19 januari 2025
    • Periode *
      Blok GS2
    • Locatie
      Eindhoven
    • Voertaal
      Engels
    Inschrijvingsperiode gesloten
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