Probabilistic Machine Learning: Advanced Topics by Kevin Murphy
Probabilistic Machine Learning: Advanced Topics - Table of Contents
- 1. Introduction
- PART I: FUNDAMENTALS
- 2. Probability
- 3. Statistics
- 4. Probabilistic 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 Techniques
- 11. Monte Carlo Methods
- 12. Markov Chain Monte Carlo (MCMC)
- 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 Regression
- 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 Probabilistic 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. Causal Inference and Structural Models
What You Will Learn in Probabilistic Machine Learning: Advanced Topics
Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy is a landmark graduate-level treatise that picks up where its introductory volume leaves off, offering an authoritative tour of modern probabilistic AI. This volume systematically breaks down cutting-edge methodologies including variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion probabilistic models, continuous-time state space models, reinforcement learning, structural causal models, and non-parametric Bayesian priors into clear, highly structured chapters.
Designed for PhD candidates, senior research scientists, quantitative analysts, and machine learning engineers, this text illuminates how advanced probabilistic frameworks handle uncertainty, high-dimensional generative modeling, and complex decision-making. Dr. Murphy bridges deep theoretical foundations with practical, state-of-the-art computational algorithms, guiding readers through approximate inference, normalizing flows, score-based models, and causal discovery. Whether training large generative diffusion networks or deriving bounds for mean-field variational inference, researchers will find this analytical coverage indispensable.
Acclaimed globally for its encyclopedic scope, visual clarity, and connection to cutting-edge research codebases, it is widely recognized as one of the best advanced probabilistic machine learning textbooks pdf available for independent study. It systematically equips scientists with the analytical tools needed for mastering frontier probabilistic AI research with confidence.
Book Details & Specifications
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 the Author: Kevin Patrick Murphy
The author Kevin Patrick Murphy
is a Senior Staff Research Scientist at Google DeepMind, specializing in probabilistic generative modeling, automated Bayesian inference, and deep learning. He holds a Ph.D. in Computer Science from UC Berkeley and was previously an Associate Professor of Computer Science and Statistics at the University of British Columbia.
A globally renowned figure in statistical machine learning, Dr. Murphy's foundational contributions span probabilistic graphical models, variational inference, and modern generative AI architectures. His comprehensive textbooks serve as benchmark references across academic computer science departments and industrial AI labs worldwide.
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