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Machine Learning：A Probabilistic Perspective

Machine Learning：A Probabilistic Perspective
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作者:
出版社: The MIT Press
2012-08
ISBN: 9780262018029
装帧: 精装
开本: 其他
纸张: 其他
分类: 外文古旧书>英文书>计算机与互联网
  • 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 Kevin P. Murphy is Associate Professor in the Department of Computer Science and in the Department of Statistics at the University of British Columbia. 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
  • 内容简介:
    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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