# Bayesian Reasoning and Machine Learning download

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

### Table of Contents

part I Inference in Probabilistic Models

chapter 1 Probabilistic Reasoning

chapter 2 Basic Graph Concepts

chapter 3 Belief Networks

chapter 4 Graphical Models

chapter 5 Efficient Inference in Trees

chapter 6 The Junction Tree Algorithm

chapter 7 Making Decisions

part II Learning in Probabilistic Models

chapter 8 Statistics for Machine Learning

chapter 9 Learning as Inference

chapter 10 Naive Bayes

chapter 11 Learning with Hidden Variables

chapter 12 Bayesian Model Selection

part III Machine Learning

chapter 13 Machine Learning Concepts

chapter 14 Nearest Neighbour Classification

chapter 15 Unsupervised Linear Dimension Reduction

chapter 16 Supervised Linear Dimension Reduction

chapter 17 Linear Models

chapter 18 Bayesian Linear Models

chapter 19 Gaussian Processes

chapter 20 Mixture Models

chapter 21 Latent Linear Models

chapter 22 Latent Ability Models

part IV Dynamical Models

chapter 23 Discrete-State Markov Models

chapter 24 Continuous-state Markov Models

chapter 25 Switching Linear Dynamical Systems

chapter 26 Distributed Computation

part V Approximate Inference

chapter 27 Sampling

chapter 28 Deterministic Approximate Inference

part VI Appendix

chapter 29 Background Mathematics

- Title: Bayesian Reasoning and Machine Learning
- Author: David Barber
- Length: 650 pages
- Edition: 1
- Language: English
- Publisher:Cambridge University Press
- Publication Date: 2012-01-31
- ISBN-10: 0521518148
- ISBN-13: 9780521518147

EU(multi): Click to download

9.8

10/29/2013

ZippyShare: Click to download

9.8

09/16/2014