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Starts 22 June 2025 11:46

Ends 22 June 2025

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Robust and Conjugate Gaussian Processes Regression

Join us to delve into robust and conjugate Gaussian process regression methods. This course will guide you through innovative techniques that preserve closed-form conditioning, offering solutions to the limitations posed by standard Gaussian Process assumptions regarding observation noise. Enhance your knowledge in the field of Artificial In.
Finnish Center for Artificial Intelligence FCAI via YouTube

Finnish Center for Artificial Intelligence FCAI

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Overview

Join us to delve into robust and conjugate Gaussian process regression methods. This course will guide you through innovative techniques that preserve closed-form conditioning, offering solutions to the limitations posed by standard Gaussian Process assumptions regarding observation noise.

Enhance your knowledge in the field of Artificial Intelligence and Computer Science with insights tailored for complex data analysis processes. Available on YouTube.

Syllabus

  • Introduction to Gaussian Processes
  • Overview of Gaussian processes
    Gaussian process regression fundamentals
    Standard assumptions about observation noise
  • Challenges in Standard Gaussian Processes
  • Limitations of Gaussian noise assumptions
    Real-world data challenges
    The impact of non-Gaussian noise
  • Robust Gaussian Process Regression
  • Definitions and concepts of robustness
    Methods for robust GP regression
    Handling heavy-tailed and non-Gaussian noise
    Case studies and applications
  • Conjugate Priors in Gaussian Processes
  • Definition and role of conjugacy in Bayesian methods
    Benefits of conjugate priors in GP
    Techniques for maintaining closed-form solutions
  • Conjugate Gaussian Process Regression
  • Combining robustness with conjugate priors
    Design of conjugate robust GP models
    Algorithmic implementation
  • Practical Applications
  • Examples in regression tasks
    Comparison with standard GP models
    Exploration of various datasets
  • Computational Considerations
  • Scalability of robust and conjugate GP
    Approximation methods for large datasets
    Software and tools for implementation
  • Case Studies and Projects
  • Analysis of state-of-the-art research
    Group project on developing a robust GP model
    Presentation of project findings
  • Conclusion and Future Trends
  • Summary of key takeaways
    Discussion on future research directions in robust GP
    Emerging applications of GP regression
  • Resources and Further Reading
  • Books, articles, and papers
    Online courses and lectures
    Open-source software libraries for GP regression

Subjects

Computer Science