week           topic

1               introduction 

2              Bayesian decision theory

3              dimensionality reduction methods

4              how to use the tools for implementation

5              approximation inference by generative model and sampling (1)

6              approximation inference by generative model and sampling (2)

7              approximation inference using variational autoencoder

8              midterm exam period

9              deep generative model (1) - Botlzman machine

10             deep generative model (2) - variational autoencoder

11             deep generative model (3) - generative adversarial network

12            implementation and presentation

13            implementation and presentation

14            implementation and presentation

15           final exam period