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

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.