Probabilistic Graphical Models

Course code: PROBMACLNR

This course is intended for people interested in Bayesian networks and probabilistic programming. The theoretical part at the beginning of the course will lead to a practical example of topic modeling using Latent Dirichlet Allocation and its non-parametric extension including hyperparameter estimation. By completing this course, the participants should be able to design and implement their own simple Bayesian networks for various problems.

194 EUR

235 EUR including VAT

The earliest date from 19.04.2023

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Course dates

Starting date: 19.04.2023

Type: Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 194 EUR

Register

Starting date: Individual

Type: In-person/Virtual

Course duration: 1 day

Language: en/cz

Price without VAT: 194 EUR

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Starting
date
Place
Type Course
duration
Language Price without VAT
19.04.2023 Virtual 1 day en/cz 194 EUR Register
Individual In-person/Virtual 1 day en/cz 194 EUR Register
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Course structure

  • Bayesian networks
  • Model representation
  • Generative vs. discriminative models
  • Statistical inference in Bayesian networks
    • Variational inference
    • Sampling
      • Rejection sampling
      • Markov Chain Monte Carlo
      • Metropolis-Hastings sampling
      • Gibbs sampling
  • Probability distributions
    • Binomial and multinomial distributions
    • Beta and Dirichlet distributions
    • Gamma distribution
  • Probabilistic programming languages
  • Practical example with topic modeling
    • Latent Semantic Analysis
    • Probabilistic Latent Semantic Analysis
    • Latent Dirichlet Allocation
  • Non-Parametric topic modelling
    • Dirichlet process
    • Chinese restaurant process and Stick breaking process
    • Non-parametric LDA
  • Hyperparameter estimation

Prerequisites

  • Basic knowledge of programing in Python
  • High school level of mathematics

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