Machine Learning: A Probabilistic Perspective by Kevin Murphy
Machine Learning: A Probabilistic Perspective - Table of Contents
- PART I: MACHINE LEARNING: THE BASICS
- 1. Introduction
- 2. Nearest Neighbour Classification
- 3. Linear Dimension Reduction
- 4. Generalisation
- 5. Linear Discriminant Analysis
- 6. Linear Parameter Models
- 7. Layered Neural Networks
- 8. Autoencoders
- 9. Data Visualisation
- PART II: INFERENCE AND LEARNING IN PROBABILISTIC MODELS
- 10. Basic Concepts in Probability
- 11. Introducing Graphical Models
- 12. Inference in Belief Networks
- 13. The Junction Tree Algorithm
- 14. Variational Learning and EM Algorithm
- APPENDICES
- A. Mathematics
- B. Graph Terminology
- C. Some Standard Distributions
- D. Bounds on Convex Functions
- E. Positive Definite Matrices and Kernel Functions
- F. Approximating Integrals
- PART III: PROBABILISTIC MODELS IN MACHINE LEARNING
- 15. Introduction to Bayesian Methods
- 16. Bayesian Regression
- 17. Logistic Regression
- 18. Naive Bayes
- 19. Mixture Models: Discrete Hidden Variables
- 20. Factor Analysis and PPCA
- 21. Independent Component Analysis
- 22. Dynamic Bayesian Networks: Discrete Hidden Variables
- 23. Dynamic Continuous Hiddens: Linear Dynamical Systems
- 24. Switching Linear Dynamical Systems
- 25. Gaussian Processes
- PART IV: APPROXIMATE INFERENCE METHODS
- 26. Sampling and MCMC Techniques
What You Will Learn in Machine Learning: A Probabilistic Perspective
Machine Learning: A Probabilistic Perspective by Kevin P. Murphy is a definitive masterpiece that unifies contemporary machine learning techniques under a rigorous probabilistic modeling framework. Rather than presenting machine learning as a collection of disconnected heuristics, this text breaks down complex theoretical topics including directed and undirected graphical models, variational inference, Markov chain Monte Carlo (MCMC), Gaussian processes, kernel methods, logistic regression, and deep neural networks into highly structured, accessible chapters.
Designed for doctoral candidates, graduate students, research scientists, and machine learning engineers, this book illuminates the unifying mathematical principles connecting frequentist estimation to fully Bayesian inference. Dr. Murphy systematically guides readers through exact and approximate inference algorithms, latent variable modeling, state-space estimation, and structural learning. Whether deriving parameter updates for hidden Markov models or evaluating modern deep generative architectures, researchers will find this analytical treatment indispensable.
Globally recognized for its encyclopedic scope, clear algorithmic presentation, and rich mathematical depth, it remains one of the best probabilistic machine learning textbooks pdf available for independent study. It systematically equips students with essential mathematical tools for mastering modern Bayesian machine learning with confidence.
Book Details & Specifications
Title:
Machine Learning: A Probabilistic Perspective by Kevin Murphy
Publisher:
The MIT Press
Year:
2012
Pages:
343
Type:
PDF
Language:
English
ISBN-10 #:
0262018020
ISBN-13 #:
978-0262018029
License:
CC BY-NC-ND 4.0
Amazon:
Amazon
About the Author: Kevin Patrick Murphy
The author Kevin Patrick Murphy
is a leading "machine learning" and "Bayesian modeling" researcher. Born in Ireland and raised in England, he studied at the "University of Cambridge" (BA), "University of Pennsylvania" (MEng), and earned his Ph.D. in Computer Science from "UC?Berkeley". He completed a postdoctoral fellowship at MIT and published numerous influential papers on probabilistic methods, graphical models, and statistical approaches to "artificial intelligence". Murphy is now a senior "research scientist" at Google. His work focuses on deep probabilistic models, reinforcement learning, and decision-making under uncertainty, helping researchers and practitioners build robust, interpretable "models" for real-world applications.
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