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Probabilistic Graphical Models

This course is intended for people interested in Bayesian networks and probabilistic programming.
Level
Designed for participants with advanced knowledge and experience
advanced
Course length
1 day
Language
 cz  eu
Course code
KT21110291
Machine learning
Category:
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Courses on a specific date with a live lecturer

Term
Language
Place
Form
?
How and where the course takes place.
Price without VAT
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: KT21110291-0002
Price without VAT
4 990 Kč
Open term
?
We will agree on a specific date together. This is a non-binding order.
Language
Place
Praha
Form
classroom
?
The course with an instructor in classroom.
Code of the course: KT21110291-0003
Price without VAT
4 990 Kč

Course description

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.

Required knowledge

  • basic knowledge of programing in Python
  • high school level of mathematics

Course content

  • 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

Lecturers

Jiří Materna
Jiří Materna

He is a machine learning specialist with experience in its applications in industry since 2007. Between 2008 and 2017, he worked at Seznam.cz, of which the last 7 years as head of the research department. He now works as a freelancer, offers the development of custom machine learning solutions, organizes the Machine Learning Prague conference and writes the ML Guru blog. 

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Contact us

News with the course

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Do you want this tailor-made course for your company?

Contact us

News with the course

Náhledový obrázek novinky
Machine Learning 18. 3. 2023
The differences between Machine learning and Artificiant inteligence

Machine learning (ML) and Artificial intelligence (AI) are related fields, but they are not the same thing. AI is a broader field that encompasses many different technologies, including machine learning. Check with us the key differences between machine learning and artificial intelligence.

Náhledový obrázek novinky
Machine Learning 3. 6. 2021
Discover the benefits of Machine Learning

Machine Learning allows companies to be efficient, search for patterns in data, automate and make decisions with minimal human intervention. Learned algorithms solve defined tasks in real time and based on input data. At the same time, they learn from the new data and adapt to changing conditions.

Why with us