Statistical view on machine learning

Seminar, winter semester 2019/2020

In this repo you will find all of the materials presented during the classes.
If you have any questions or need some help with projects, you can reach me via USOS.

Credit rules

1. 4 absences are permitted.
2. Projects:
   • You will work in pairs.
   • Each pair will be given one publication to work on.
   • You should prepare presentation of both paper and your reproduction of the results.
   • You will present your work in classes.

Schedule

• 15.10 - Logistic Regression and Neural Networks
• 22.10 - PyTorch tutorial
• 29.10 - Lecture by prof. Bogdan
• 05.11 - Autoencoders
• 12.11 - Variational Autoencoders https://arxiv.org/abs/1312.6114
• 19.11 - beta-VAE https://openreview.net/pdf?id=Sy2fzU9gl
• 26.11 - Generative Adversarial Networks https://arxiv.org/abs/1406.2661
• 03.12 - BiGAN https://arxiv.org/abs/1605.09782
• 17.12 - Introduction to Convolutional Neural Networks
• 14.01 - CGAN https://arxiv.org/abs/1411.1784
• 21.01 - AAE https://arxiv.org/abs/1511.05644
• 28.01 - Normalizing Flows:
  • NICE https://arxiv.org/abs/1410.8516
  • Real NVP https://arxiv.org/abs/1605.08803
  • Conditional Invertible Flow (CIF) https://arxiv.org/abs/1910.07344
• 04.02 - Invited talks:
  • Agata Właszczyk - Neuroscience view on machine learning
  • Maciej Zięba - My story at Tooploox