Designing with advanced artificial intelligence

DBM180

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

Prior knowledge

You must meet the following requirements

Resources

  • 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.)

Additional information

  • Credits
    ECTS 5
  • Level
    master
  • Selection course
    No
If anything remains unclear, please check the FAQ of TU Eindhoven.

Offering(s)

  • Start date

    11 November 2024

    • Ends
      19 January 2025
    • Term *
      Block GS2
    • Location
      Eindhoven
    • Instruction language
      English
    Course is currently running
For guests registration, this course is handled by TU Eindhoven