The following lectures and tutorials have been recorded and can be found on our YouTube channel.
- Day 1 - Introduction to Probabilistic Modelling
- Day 2 - Variational Inference and Optimization 1
- Day 3 - Variational Inference and Optimization 2
- Day 4 - Introduction to Deep Learning & Deep Generative Models
- Day 5 - Deep Generative Models
- Day 1 - Probabilistic Programming
- Day 2 - Variational Inference and Probabilistic Programming 1
- Day 3 - Variational Inference and Probabilistic Programming 2
- Day 4 - Deep Latent Variable Models for Imputation of Incomplete Data Sets (Imputations with MIWAE)
- Day 5 - Bayesian Sparsification of Neural Networks
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.
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 - Day 1 -
students_Bayesian_regression.ipynb - Day 2 -
students_simple_model.ipynb - Day 2 -
students_lin_reg.ipynb - Day 3 -
student_simple_model.ipynb - Day 3 -
student_BBVI.ipynb - Day 3 -
Bayesian_linear_regression.ipynb - Day 3 -
VAE.ipynb - Day 3 -
FA.ipynb
- Day 2:
- Evolving Deep Neural Networks by Keith L. Downing
- Day 3:
- Value of Information by Jo Eidsvik
- Bayesian Methods for Rank and Preference Data: From Recommender Systems to Cancer Genomics by Valeria Vitelli
- Day 4:
- Combining Model and Parameter Uncertainty in BNNs by Aliaksandr Hubin