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Machine Learning II 2018/19 spring

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VIII. Maximum likelihood and maximum a posteriori estimates

Given by Sven Laur

Brief summary: Formalisation of Maximum Likelihood and Maximum A Posteriori principles. Simple examples of ML and MAP estimates. Probabilistic model for linear regression and corresponding maximum likelihood solution. Link functyion. Connection between regularisation and Maximum A Posteriori estimate. Regularised linear models and corresponding priors to parameters.

Slides: PDF

Video: UTTV(2016) UTTV(2015)

Literature:

  • Duda, Hart & Stork: Patter Classification pages 84-107
  • Bishop: Pattern Recognition and Machine Learning pages 137 - 161
  • Bishop: Pattern Recognition and Machine Learning pages 204 - 220

Complementary exercises:

  • Bishop: Pattern Recognition and Machine Learning pages 173 - 177
  • Bishop: Pattern Recognition and Machine Learning pages 220 - 224
  • Practical comparison of various linear regression methods on data with different error distributions.

Free implementations:

  • Built-in stats package in R: glm anova.
  • LARS package in R
  • Quantreg package in R: rq
  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
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