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 24.10.2022

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Course dates

Starting date: 24.10.2022

Guaranteed

Type: Individual

Course duration: 1 day

Language: cz

Price without VAT: 194 EUR

Register

Starting date: Individual

Type: Individual

Course duration: 1 day

Language: cz

Price without VAT: 194 EUR

Register

Starting
date
Place
Type Course
duration
Language Price without VAT
G 24.10.2022 Individual 1 day cz 194 EUR Register
Individual Individual 1 day cz 194 EUR Register
G Guaranteed course

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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

Do you need advice or a tailor-made course?

daniel

Daniel Šťastný

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