Machine learning : a probabilistic perspective /
"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as...
Guardado en:
| Autor principal: | |
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| Formato: | Libro |
| Lenguaje: | Inglés |
| Publicado: |
Cambridge, MA :
MIT Press,
c2012.
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| Colección: | Adaptive computation and machine learning
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| Materias: | |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
Tabla de Contenidos:
- Probability
- Generative models for discrete data
- Gaussian models
- Bayesian statistics
- Frequentist statistics
- Linear regression
- Logistic regression
- Generalized linear models and the exponential family
- Directed graphical models (Bayes nets)
- Mixture models and the EM algorithm
- Latent linear models
- Sparse linear models
- Kernels
- Gaussian processes
- Adaptive basis function models
- Markov and hidden Markov models
- State space models
- Undirected graphical models (Markov random fields)
- Exact inference for graphical models
- Variational inference
- More variational inference
- Monte Carlo inference
- Markov chain Monte Carlo (MCMC) inference
- Clustering
- Graphical model structure learning
- Latent variable models for discrete data
- Deep learning
- Notation.