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Materials of the Nordic Probabilistic AI School 2019.

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- - environment.yml
  - README.md
  - slides-L1.pdf
  - solutions_bayesian_regression.ipynb
  - solutions_PPLs_Intro.ipynb
  - students_Bayesian_regression.ipynb
  - students_PPLs_Intro.ipynb
- introduction_to_probabilistic_modelling.pdf
- - environment.yml
  - README.md
  - slides-L2.pdf
  - solution_lin_reg.ipynb
  - solution_simple_model.ipynb
  - students_lin_reg.ipynb
  - students_simple_model.ipynb
- evolving_deep_neural_networks.pdf
- variational_inference_and_optimization_1.pdf
- - Bayesian_linear_regression.ipynb
  - BBVI-gradient-variance.eps
  - BBVI_exercise.png
  - elbo_evolution.pdf
  - elbo_evolution_with_1_samples.pdf
  - environment.yml
  - FA.ipynb
  - FA_model.png
  - README.md
  - reg_model.png
  - simple_pyro_exercise.png
  - slides-L3.pdf
  - solution_BBVI.ipynb
  - solution_simple_model.ipynb
  - student_BBVI.ipynb
  - student_simple_model.ipynb
  - VAE.ipynb
- bayesian_methods_for_rank_and_preference_data.pdf
- Klami_ProbAISchool_2.pdf
- value_of_information.pdf
- - dlvm_missing.pdf
  - MIWAE_Pytorch_exercises_demo_ProbAI.ipynb
  - MIWAE_Pytorch_exercises_demo_ProbAI_solutions.ipynb
- combining_model_and_parameter_uncertainty_in_bnns.pdf
- slides-deep-generative-models-thomas-lucas.pdf
- - Bayesian_sparsification_tutorial.ipynb
  - Bayesian_sparsification_tutorial_solution.ipynb
  - BayesianSparsificationTutorial.pdf
  - logger.py
  - readme.md
- dgm_thomas_lucas.pdf
README.md

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# ProbAI 2019

Recorded lectures and tutorials, slides and program code from the very first Nordic Probabilistic AI School ([ProbAI 2019](https://2019.probabilistic.ai)).

## Lectures and Tutorials

* Day 1 (June 3):
  * [[Lecture](https://youtu.be/rvB-VVgot2A), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day1/introduction_to_probabilistic_modelling.pdf)] Introduction to Probabilistic Modelling
  * [[Tutorial](https://youtu.be/JWHn2fdw5YA), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day1/day1_tutorial)] Probabilistic Programming
* Day 2 (June 4):
  * [[Lecture](https://youtu.be/60USDNc1nE8), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day2/variational_inference_and_optimization_1.pdf)] Variational Inference and Optimization 1
  * [[Tutorial](https://youtu.be/iELH5hlk92o), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day2/day2_tutorial)] Variational Inference and Probabilistic Programming 1
* Day 3 (June 5):
  * [[Lecture](https://youtu.be/r_YzsRW0bDs), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day3/Klami_ProbAISchool_2.pdf)] Variational Inference and Optimization 2
  * [[Tutorial](https://youtu.be/IsXba_krm7A), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day3/day3_tutorial)] Variational Inference and Probabilistic Programming 2
* Day 4 (June 6):
  * [[Lecture](https://youtu.be/W_8RbgkwXDc), [Materials](https://github.com/probabilisticai/probai-2019/blob/master/day4/slides-deep-generative-models-thomas-lucas.pdf)] Introduction to Deep Learning & Deep Generative Models
  * [[Tutorial](https://youtu.be/xeGoURlbF-c), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day4/tutorial_dlvm)] Deep Latent Variable Models for Imputation of Incomplete Data Sets (Imputations with MIWAE)
* Day 5 (June 7):
  * [[Lecture](https://youtu.be/_Hy7Xkrmk7Y), [Materials](https://github.com/probabilisticai/probai-2019/blob/master/day5/dgm_thomas_lucas.pdf)] Deep Generative Models
  * [[Tutorial](https://www.youtube.com/watch?v=aW-tMXJ5a7s), [Materials](https://github.com/probabilisticai/probai-2019/tree/master/day5/Bayesian_sparsification_tutorial)] Bayesian Sparsification of Neural Networks

###  Google Colab

If your local computer doesn't have all the software packages and you are not able to finish the installation, you can try [Google Colab](https://colab.research.google.com/).

Start the Google Colab notebook with the following line to install the necessary packages `!pip install -q --upgrade pyro-ppl torch`.

* Day 1 - [`students_PPLs_Intro.ipynb`](https://colab.research.google.com/drive/1JpAyz_0vAfFA50_ufKsJ4lVUmeOYRfms)
* Day 1 - [`students_Bayesian_regression.ipynb`](https://colab.research.google.com/drive/1CkFBxzP9Y43q6uxI0cLGiRsJ8vuUBfYD)
* Day 2 - [`students_simple_model.ipynb`](https://colab.research.google.com/github/PGM-Lab/probabilisticAI_tutorials/blob/master/Day2/students_simple_model.ipynb#scrollTo=BSb_JL4qXAd-)
* Day 2 - [`students_lin_reg.ipynb`](https://colab.research.google.com/github/PGM-Lab/probabilisticAI_tutorials/blob/master/Day2/students_lin_reg.ipynb)
* Day 3 - [`student_simple_model.ipynb`](https://colab.research.google.com/drive/16O1ifMkGtVMd8HCnyST2nw61CDO7Olaa)
* Day 3 - [`student_BBVI.ipynb`](https://colab.research.google.com/github/PGM-Lab/probabilisticAI_tutorials/blob/master/Day3/student_BBVI.ipynb)
* Day 3 - [`Bayesian_linear_regression.ipynb`](https://colab.research.google.com/drive/1fgTv9yJ0TC1x-2mr0MUega6mx9JUzZbL)
* Day 3 - [`VAE.ipynb`](https://colab.research.google.com/drive/1YQRcOLreMt4DXayq-V8qF7q676aVGVRT)
* Day 3 - [`FA.ipynb`](https://colab.research.google.com/drive/1dNjmZJYUjPLT40heW0TCU0A3cl96iUYm)

## Talks

* Day 2:
    * [Evolving Deep Neural Networks](https://github.com/probabilisticai/probai-2019/blob/master/day2/evolving_deep_neural_networks.pdf) by Keith L. Downing
* Day 3:
    * [Value of Information](https://github.com/probabilisticai/probai-2019/blob/master/day3/value_of_information.pdf) by Jo Eidsvik
    * [Bayesian Methods for Rank and Preference Data: From Recommender Systems to Cancer Genomics](https://github.com/probabilisticai/probai-2019/blob/master/day3/bayesian_methods_for_rank_and_preference_data.pdf) by Valeria Vitelli
* Day 4:
    * [Combining Model and Parameter Uncertainty in BNNs](https://github.com/probabilisticai/probai-2019/blob/master/day4/combining_model_and_parameter_uncertainty_in_bnns.pdf) by Aliaksandr Hubin

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