Bayesian Reasoning and Machine Learning by David Barber
Bayesian Reasoning and Machine Learning - Table of Contents
Part I: Inference in Probabilistic Models
- 1. Probabilistic Reasoning
- 2. Basic Graph Concepts
- 3. Belief Networks
- 4. Graphical Models
- 5. Efficient Inference in Trees
- 6. Junction Tree Algorithm
- 7. Making Decisions
Part II: Learning in Probabilistic Models
- 8. Statistics for Machine Learning
- 9. Learning as Inference
- 10. Naive Bayes
- 11. Learning with Hidden Variables
- 12. Bayesian Model Selection
Part III: Machine Learning
- 13. Machine Learning Concepts
- 14. Nearest Neighbour Classification
- 15. Unsupervised Linear Dimension Reduction
What You Will Learn in Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning by David Barber is an authoritative, modern textbook designed for advanced undergraduate and graduate students in computer science, data science, and artificial intelligence. Serving as an essential open academic reference, this volume provides a rigorous framework for bayesian reasoning and machine learning problem solving practice.
The textbook systematically covers fundamental probability distributions, graphical models, belief networks, dynamic Bayesian networks, expectation-maximization algorithms, variational inference, and Markov Chain Monte Carlo (MCMC) methods. By bridging probabilistic graphical modeling with modern machine learning techniques, Barber enables readers to execute complex probabilistic inference and approximate algorithms with precision.
Treasured globally by AI researchers and data engineers for its algorithmic clarity, comprehensive exercises, and practical examples, this modern monograph serves as an outstanding self-study guide. Barber’s clear exposition equips learners with vital mathematical tools required for mastering modern probabilistic machine learning principles.
Book Details & Specifications
Title:
Bayesian Reasoning and Machine Learning by David Barber
Publisher:
Cambridge University Press
Year:
2025
Pages:
768
Type:
PDF
Language:
English
ISBN-10 #:
0521518148
ISBN-13 #:
978-0521518147
License:
External Educational Resource
Amazon:
Amazon
About the Author: David Barber
The author David Barber
David Barber is a Professor of Information Processing in the Department of Computer Science at University College London (UCL) and Director of the UCL Centre for Artificial Intelligence, renowned for his research in probabilistic modelling, machine learning, and neural networks.
His academic textbooks stand as foundational bayesian machine learning, graphical models, and artificial intelligence study guides.
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