Математические методы прогнозирования (практика, В.В. Стрижов)/Группа 574, осень 2019
Материал из MachineLearning.
(Различия между версиями)
| Строка 8: | Строка 8: | ||
* Seminar 1 (Isachenko) | * Seminar 1 (Isachenko) | ||
** Plate notation and Bayesian inference in examples | ** Plate notation and Bayesian inference in examples | ||
| + | ** (reminder Coherent Bayesian Inference) | ||
** Variational inference | ** Variational inference | ||
** Variational autoencoder | ** Variational autoencoder | ||
** ELBO | ** ELBO | ||
* Seminar 6 (Isachenko) | * Seminar 6 (Isachenko) | ||
| + | ** Analytic methods of approximation | ||
| + | ** Statistic sum approximation | ||
** Generative versus discriminative | ** Generative versus discriminative | ||
* Seminar 7 (Isachenko) | * Seminar 7 (Isachenko) | ||
| - | ** Zoo of variational autoencoders | + | ** Inference methods of approximation |
| + | ** Zoo of variational autoencoders and practical examples | ||
* Seminar 8 (Isachenko) | * Seminar 8 (Isachenko) | ||
| - | ** | + | ** Generative adversarial networks |
* Seminar 2 (Bakhteev) | * Seminar 2 (Bakhteev) | ||
** Methods of model selection | ** Methods of model selection | ||
Версия 15:04, 28 августа 2019
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This series of seminars continues the course Bayesian model selection and investigates the theoretical aspects of model selection in various application problems.
- Seminar 1 (Isachenko)
- Plate notation and Bayesian inference in examples
- (reminder Coherent Bayesian Inference)
- Variational inference
- Variational autoencoder
- ELBO
- Seminar 6 (Isachenko)
- Analytic methods of approximation
- Statistic sum approximation
- Generative versus discriminative
- Seminar 7 (Isachenko)
- Inference methods of approximation
- Zoo of variational autoencoders and practical examples
- Seminar 8 (Isachenko)
- Generative adversarial networks
- Seminar 2 (Bakhteev)
- Methods of model selection
- Generalization theorem
- Seminar 3 (Bakhteev)
- Complexity theorems
- Seminar 4 (Grabovoy?)
- Mixture of experts
- Priors on the mixture
- Privileged learning and distilling
- Seminar 5 (Aduenko?)
- Theorem of number of experts
- Seminar 9 (Vladimirova?)
- Prior propagation for deep learning networks
- Seminar 10
- Directional Bayesian statistics
- Seminar 11
- Bayesian structure learning
- Seminar 12
- Probabilistic metric space construction
- Seminar 13
- Informative prior
- Seminar 14
- Bayesian programming
- Informative prior with applications
