Schedule
- 2nd of September: The choice of the course book
- 9th of September: Deep Learning Frameworks (Sven Laur)
- 16th of September: Feed-Forward neural network architectures (Elena Novikova)
- 23th of September: Recurrent neural network architectures (Õie Renata Siimon)
- 30th of September: Optimisation methods (Wai Tik Chan & Anhelina Lohvina)
- 7th of October: Maximum likelihood and loss functions (Mihkel Lepson)
- 14th of October: Probabilistic models for continuous data (Pavel Chizhov & Denys Kaliuzhnyi)
- 21th of October: Probabilistic models for count data (???)
- 28th of October: Mixture models (Jan-Martin Tamm)
- 4th of November: Normalising flows (Novin Shahroudi)
- 11th of November: Basics of Bayesian inference (Karl Kaspar Haavel)
- 18th of November Variational inference (???)
- 25th of November: Monte-Carlo dropout (Joonas Järve)
- 2nd of December: Markov-Chain-Monte-Carlo methods (???)