Ruben Martinez-Cantin

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Introduction to Gaussian Processes and Active Learning:
Regression, Classification, Experimental Design and Bayesian Optimization.

Practical session at the 18th Machine Learning Summer School.

During the first part of the lab, we will overview some of the capabilities and methods to do learning and inference using Gaussian processes (GPs). A second, more advanced, part of the lab will cover the problems of active learning, experimental design, Bayesian optimization and submodular optimization based on GPs. During both labs, we will illustrate each part with realistic learning problems such as: robot kinematics, handwritten digits recognition or optimal sensor placement.
Required software: Matlab or Octave.

Downloads:

• Toolboxes and datasets
• Handouts:
  • Regression and classification
  • Active learning and Bayesian optimization
• Code:
  • Regression, Exercise 1, Exercise 2
  • Classification, Exercise 1, Mat2gray
  • Active learning and Bayesian optimization
    • Continuous inputs: Exercise 1, Exercise 2, Example criterion
    • Discrete inputs: Example.
• Slides
  • Regression
  • Classification
  • Active learning - Continuous Inputs
  • Active learning - Discrete Inputs
• Rasmussen and Williams, Gaussian Processes for Machine Learning (online version)