TransferLab Training: Bayesian methods in ML (english, on-site)

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TransferLab Training: Bayesian methods in ML (english, on-site)

Do you want to become an AI expert? Now you have the opportunity to gain significant knowledge for you and your company.

When and where

Date and time


Ipai Campus Zukunftspark 7 74076 Heilbronn Germany

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About this event

  • 1 day 8 hours
  • Mobile eTicket

Dear all,

We would like to invite you to this workshop introducing basic Bayesian modelling, using practical examples and probabilistic programming.

Part 1

  • Introduction to Bayesian methods and probabilistic programming languages.
  • Inference with discrete variables: Who has the right skill set for your job offering?
  • Inference with Markov chain Monte Carlo methods: Marriages, divorces and causality.
  • The Lotka-Volterra predator-prey model. Advantages and necessity of hybrid approaches combining deterministic and statistical models.

Part 2

  • Stochastic variational inference and the mean field assumption.
  • Clustering images with mixture models and latent Dirichlet allocation like models.
  • Understanding the development of asthma through Markov chains.
  • Become a wine connoisseur with Bayesian neural networks.
  • Modeling faces through the ages with variational autoencoders.

Learning Outcomes

  • Understanding the Bayesian methodology, and the foundations of approximate bayesian inference.
  • First steps in probabilistic programming in pyro.
  • Getting to know the model based machine learning approach:
  • Learn to make the right assumptions.
  • Criticise your own model.
  • Getting to know many model types and comparing their performance.
  • Applying these models to a wide range of areas.
  • Combining probabilistic modelling with deep learning.

Trainer: Dr. Faried Abu Zaid, Senior AI Researcher at appliedAI, Ivan Rodriguez, Senior AI Engineer at appliedAI

Duration for both Training days: 9am-5pm with lunch and coffee breaks. Drinks and Snacks will be provided. This Training will be held in English.

Looking forward to seeing you there.