Probabilistic Machine Learning: Advanced Topics by Kevin Murphy
Book Contents :-
1. Introduction
PART I: FUNDAMENTALS
2. Probability
3. Statistics
4. Graphical Models
5. Information Theory
6. Optimization
PART II: INFERENCE
7. Inference Algorithms: An Overview
8. Gaussian Filtering and Smoothing
9. Message Passing Algorithms
10. Variational Inference
11. Monte Carlo Methods
12. Markov Chain Monte Carlo
13. Sequential Monte Carlo
PART III: PREDICTION
14. Predictive Models: An Overview
15. Generalized Linear Models
16. Deep Neural Networks
17. Bayesian Neural Networks
18. Gaussian Processes
19. Beyond the IID Assumption
PART IV: GENERATION
20. Generative Models: An Overview
21. Variational Autoencoders
22. Autoregressive Models
23. Normalizing Flows
24. Energy-Based Models
25. Diffusion Models
26. Generative Adversarial Networks
PART V: DISCOVERY
27. Discovery Methods: An Overview
28. Latent Factor Models
29. State-Space Models
30. Graph Learning
31. Nonparametric Bayesian Models
32. Representation Learning
33. Interpretability
PART VI: ACTION
34. Decision Making Under Uncertainty
35. Reinforcement Learning
36. Causality
About this book :-
"Probabilistic Machine Learning: Advanced Topics" by "Kevin Patrick Murphy" is a comprehensive guide to "probabilistic machine learning", focusing on advanced models and techniques for reasoning under uncertainty. The book builds on foundational concepts and explores complex "graphical models", latent variable methods, and structured prediction. It balances "theory" and practice, helping readers understand both the mathematics and practical applications of probabilistic approaches.
The book covers key "inference" techniques, including approximate methods, Monte Carlo sampling, variational inference, and message-passing algorithms. It also dives into "Bayesian methods", expectation-maximization, and advanced learning algorithms that allow models to handle high-dimensional and structured data efficiently. Real-world examples in natural language processing, computer vision, and time-series modeling illustrate how probabilistic reasoning can improve predictions and decision-making.
Designed for graduate students, researchers, and experienced practitioners, the book emphasizes mathematical rigor while providing actionable insights for implementing complex models. By integrating probabilistic thinking into "machine learning", readers gain tools to model uncertainty, capture dependencies, and build principled predictive systems. It is an essential resource for anyone looking to advance their understanding of modern "probabilistic modeling" and apply it to real-world data challenges.
Book Detail :-
Title:
Probabilistic Machine Learning: Advanced Topics by Kevin Murphy
Publisher:
The MIT Press
Year:
2023
Pages:
1370
Type:
PDF
Language:
English
ISBN-10 #:
0262048434
ISBN-13 #:
978-0262048439
License:
CC BY-NC-ND 4.0
Amazon:
Amazon
About Author :-
The author
Kevin Patrick Murphy
is a leading "machine learning" and "Bayesian modeling" expert. Born in Ireland and raised in England, he studied at the "University of Cambridge", "University of Pennsylvania", and earned his Ph.D. in Computer Science from UC?Berkeley. Murphy has authored foundational textbooks and research papers, focusing on probabilistic methods, generative models, and statistical approaches to "artificial intelligence". Currently a senior "research scientist" at Google, he works on deep probabilistic models, reinforcement learning, and decision-making under uncertainty. His work bridges classical "statistics" with modern AI, helping researchers and practitioners build robust, interpretable "models" for complex real-world problems.
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