Pattern recognition and machine learning
Bishop, Christopher M.
Pattern recognition and machine learning Christopher M Bishop - New York : Springer, ©2006. - 738 pages
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- . Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models
Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
9781493938438
006.4 / BIS
Pattern recognition and machine learning Christopher M Bishop - New York : Springer, ©2006. - 738 pages
Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- . Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models
Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
9781493938438
006.4 / BIS