bayesian reasoning and machine learning

Download Book Bayesian Reasoning And Machine Learning in PDF format. You can Read Online Bayesian Reasoning And Machine Learning here in PDF, EPUB, Mobi or Docx formats.

Bayesian Reasoning And Machine Learning

Author : David Barber
ISBN : 9780521518147
Genre : Computers
File Size : 51. 88 MB
Format : PDF, Mobi
Download : 734
Read : 250

Download Now


A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Bayesian Reasoning And Machine Learning

Author : David Barber
ISBN : 9781139643207
Genre : Computers
File Size : 30. 77 MB
Format : PDF, Docs
Download : 578
Read : 1001

Download Now


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.

Bayesian Reasoning And Machine Learning

Author : David Barber
ISBN : 1139636065
Genre : Bayesian statistical decision theory
File Size : 53. 18 MB
Format : PDF, ePub, Mobi
Download : 482
Read : 593

Download Now


"Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online"--

Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262018029
Genre : Computers
File Size : 48. 36 MB
Format : PDF, Docs
Download : 723
Read : 818

Download Now


A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Bayesian Time Series Models

Author : David Barber
ISBN : 9780521196765
Genre : Computers
File Size : 54. 19 MB
Format : PDF, Mobi
Download : 926
Read : 235

Download Now


The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Probabilistic Graphical Models

Author : Daphne Koller
ISBN : 9780262258357
Genre : Computers
File Size : 23. 62 MB
Format : PDF, ePub, Mobi
Download : 456
Read : 446

Download Now


Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Modeling And Reasoning With Bayesian Networks

Author : Adnan Darwiche
ISBN : 9780521884389
Genre : Computers
File Size : 72. 86 MB
Format : PDF, Docs
Download : 290
Read : 1067

Download Now


This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Information Theory Inference And Learning Algorithms

Author : David J. C. MacKay
ISBN : 0521642981
Genre : Computers
File Size : 43. 64 MB
Format : PDF
Download : 696
Read : 1275

Download Now


Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.

Learning Bayesian Networks

Author : Richard E. Neapolitan
ISBN : STANFORD:36105111872318
Genre : Computers
File Size : 56. 85 MB
Format : PDF, ePub, Docs
Download : 151
Read : 248

Download Now


For courses in Bayesian Networks or Advanced Networking focusing on Bayesian networks found in departments of Computer Science, Computer Engineering and Electrical Engineering. Also appropriate as a supplementary text in courses on Expert Systems, Machine Learning, and Artificial Intelligence where the topic of Bayesian Networks is covered. This book provides an accessible and unified discussion of Bayesian networks. It includes discussions of topics related to the areas of artificial intelligence, expert systems and decision analysis, the fields in which Bayesian networks are frequently applied. The author discusses both methods for doing inference in Bayesian networks and influence diagrams. The book also covers the Bayesian method for learning the values of discrete and continuous parameters. Both the Bayesian and constraint-based methods for learning structure are discussed in detail.

Pattern Recognition And Machine Learning

Author : Christopher M. Bishop
ISBN : 1493938436
Genre : Computers
File Size : 35. 45 MB
Format : PDF, ePub, Docs
Download : 993
Read : 994

Download Now


This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. 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.

Top Download:

Best Books