## Machine Learning: A Probabilistic PerspectiveToday'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 copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. |

### What people are saying - Write a review

i need solution of this book excersize

### Contents

1 Introduction | 1 |

2 Probability | 27 |

3 Generative Models for Discrete Data | 65 |

4 Gaussian Models | 97 |

5 Bayesian Statistics | 149 |

6 Frequentist Statistics | 191 |

7 Linear Regression | 217 |

8 Logistic Regression | 245 |

18 State Space Models | 631 |

19 Undirected Graphical Models Markov Random Fields | 661 |

20 Exact Inference for Graphical Models | 707 |

21 Variational Inference | 731 |

22 More Variational Inference | 767 |

23 Monte Carlo Inference | 815 |

24 Markov Chain Monte Carlo MCMC Inference | 837 |

25 Clustering | 875 |

9 Generalized Linear Models and the Exponential Family | 281 |

10 Directed Graphical Models Bayes Nets | 307 |

11 Mixture Models and the EM Algorithm | 337 |

12 Latent Linear Models | 381 |

13 Sparse Linear Models | 421 |

14 Kernels | 479 |

15 Gaussian Processes | 515 |

16 Adaptive Basis Function Models | 543 |

17 Markov and Hidden Markov Models | 589 |

26 Graphical Model Structure Learning | 907 |

27 Latent Variable Models for Discrete Data | 945 |

28 Deep Learning | 995 |

Notation | 1009 |

1015 | |

1047 | |

1050 | |