Artificial Intelligence, Creativity, and the Arts

Universidad Pompeu Fabra

Course Description

  • Course Name

    Artificial Intelligence, Creativity, and the Arts

  • Host University

    Universidad Pompeu Fabra

  • Location

    Barcelona, Spain

  • Area of Study

    Computer Science, Dance, Music, Studio Art

  • Language Level

    Taught In English

    Hours & Credits

  • ECTS Credits

  • Recommended U.S. Semester Credits
  • Recommended U.S. Quarter Units
  • Overview

    Week 1: Introduction
    o What is artificial intelligence and machine learning? 
    o What is it good for?
    o Artificial intelligence in the arts
    o Artistic and interactive applications in which AI can be a tool for creativity
    Week 2: Classification, Part I
    o Basics of classification,
    o How to use it to make sense of complex data in a meaningful way?
    o Instance-based learning: the nearest-neighbour algorithm
    Week 3: Classification, Part II
    o Interpretable AI: the decision trees algorithm
    o AI free open source tools: Weka, Wekinator
    o Real-time AI for artistic creativity
    Week 4: Regression, Part I
    o Fundamentals of regression
    o How can we use it for creating continuous mapping and controls?
    o Linear regression
    o polynomial regression
    Week 5: Regression, Part II
    o neural networks
    o How to use regression algorithms to create new types of interactions?
    o Hands-on practice exploring regression algorithms
    o Application of regression algorithms to build your own systems
    Week 6: Design Considerations
    o what it means to build a good classifier
    o Decision trees (revisited)
    o Support vector machines
    o how learning algorithms can be integrated into your own creative work
    o Application of classification algorithms to build your own projects
    Week 7: Sensors and Features
    o How to apply machine learning to work with multimodal real-time data,
    o Audio, video, game controllers, and sensors
    o Making sense of the data from different inputs
    o Designing feature extractors that make machine learning easier
    o developing feature extractors
    Week 8: Working with Time
    o Algorithms for data over time
    o Dynamic time warping
    o Gesture following
    o Gesture following in the arts
    o Appling temporal modeling algorithms to real-time sensor data
    Week 9: Machine Learning Practice in Practice and Project
    o Overfitting
    o Error analysis
    o Project design and implementation
    Week 10: Health and Well-being implications of the arts
    o Art, music and health
    o Art Therapy
    o Music Therapy
    o Implications in stroke rehabilitation, emotional disorders, autism

Course Disclaimer

Courses and course hours of instruction are subject to change.


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