Materials of the Probabilistic AI School 2026, occurring on 3–7 August 2026 in Vilnius (Lithuania).
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Day 1 (Aug 3):
- Silja Renooij - Introduction to Probabilistic Models
- Eliezer de Souza da Silva - Bayesian Workflow and PPLs [Slides & Material]
- Notebooks:
- Code-along notebook [Regression (github)] + [Regression (Colab)]
- Code-along [SIR / Linear Dynamic Models (github)] + [SIR / Linear Dynamic Models (Colab)]
- Exercises [Student Version (github) ] + [Student Version (Colab)]
- [Slides]
- Notebooks:
- Gintare Karolina Dziugaite - Dynamics of Memorization and Generalization in Deep Learning [Slides]
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Day 2 (Aug 4):
- Helge Langseth and Thomas D. Nielsen - Variational Inference and Optimization [Slides & Material]
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Day 3 (Aug 5):
- Jonas Arruda - Simulation-Based Inference [Slides & Material]
- Sara Magliacane - Causal Machine Learning [Slides & Material]
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Day 4 (Aug 6):
- Claire Vernade - Contextual Bandits [Slides] [Notebook (Colab)]
- Zita Marinho - Reinforcement Learning [Slides & Material]
- Mantautas Rimkus - Experimentation @Vinted [Slides]
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Day 5 (Aug 7):
- Dimitri Meunier - Kernel Methods [Slides]
- Rūta Binkyte - Bias and Safety in AI [Slides] [Notebook (Colab)]
For the detailed program, please visit our website.
Project assignment at https://github.com/probabilisticai/dt8122-2026.
For all your questions or suggestions, please contact us at hello@probabilistic.ai.