Probabilistic modelling

Probabilistic modelling, learning and algorithms

Probabilistic modelling, learning and algorithms

Duration

2-5 h

Syllabus

  • Motivation: supervised and unsupervised learning, topic models
  • The EM algorithm
  • Gibbs sampling
  • Variational inference
  • Hyperparameter learning
  • Practical topic modelling
  • Some software implementations

Prerequisites

Some statistical and Python background

Credential

Taught in executive (ad-hoc) courses, and also part of ‘Statistical modelling and inference’ in MSc Data Science

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Data Sciencce Center Barcelona Graduate School of Economics Ramón Trías Fargas, 25-27 08005 Barcelona, Spain.

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