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. T
【作者简介】
Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia.
【目录】
Chapter 1: Introduction Chapter 2: Probability Chapter 3: Statistics Chapter 4: Gaussian models Chapter 5: Generative models for classification Chapter 6: Discriminative linear models Chapter 7: Graphical Models Chapter 8: Decision theory Chapter 9: Mixture models and the EM algorithm Chapter 10: Latent Linear models Chapter 11: Hierarchical Bayes Chapter 12: Sparce Linear Models Chapter 13: Kernels Chapter 14: Gaussian processes Chapter 15: Adaptive basis function models Chapter 16: Markov and hidden Markov Models Chapter 17: State space models Chapter 18: Conditional random fields Chapter 19: Exact inference algorithms for graphical models Chapter 20: Mean field inference algorithms Chapter 21: Other variational inference algorithms Chapter 22: Monte Carlo inference algorithms Chapter 23: MCMC inference algorithms Chapter 24: Clustering Chapter 25: Graphical model structure learning Chapter 26: Two-layer latent variable models Chapter 27: Deep learning
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