Probabilistic machine learning : advanced topics /
Un libro avanzado para investigadores y estudiantes de posgrado que trabajan en aprendizaje automático y estadística y quieren aprender sobre aprendizaje profundo, inferencia bayesiana, modelos generativos y toma de decisiones bajo incertidumbre. Una contrapartida avanzada a Probabilistic Machine L...
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Mass. :
The MIT Press,
©2023
|
| Series: | Adaptive computation and machine learning
|
| Subjects: | |
| Online Access: | Información sobre el autor |
Table of Contents:
- Introduction
- 1. Fundamentals: probability. Statistics. Graphical models. Information theory. Optimization
- 2. Inference: Inference algothms. Gaussian filtering and smoothind. Message passing algorithms. Variational inference. Monte Carlo methods. Markov chain Monte Carlo. Sequential Monte Carlo
- 3. Prediction: Predictive models. Generalized lineal models. Deep neural networks. Bayesian neural networks. Gaussian processes. Beyond the iid assumption
- 4. Generation: Generative models. Variational autoencoders. Autoregressive models. Normalizing flows. Enery-based models. Diffusion models. Generative adversarial networks
- 5. Discovery: Latent factor models. State-space models. Graph learning. Nonparametric bayesian models. Representation learning. Interpretability
- 6. Action: Decision making under uncertainty. Reinforcement learning. Causality.