| Description |
xxix, 1067 pages : illustrations (chiefly colour) ; 24 cm |
| Series |
Adaptive computation and machine learning series |
|
Adaptive computation and machine learning series
|
| Contents |
Probability.--Generative models for discrete data.--Gaussian Models.--Frequentist Statistics.--Linear regression.--Logistic Regression.--Generalized linear models and the exponential family.--Directed graphical models (Bayes nets).--Mixture models and the EM algorithm.--Latnet 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.-- |
| Summary |
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. 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 well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are illustrated with images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. The book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package. PMTK (probabilistic modeling toolkit), that is freely available online |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Machine learning.
|
|
Probabilities.
|
| LC no. |
2012004558 |
| ISBN |
9780262018029 (hardback) |
|
0262018020 (hardback) |
|
