Probabilistic Machine Learning: An Introduction by Kevin Murphy
Probabilistic Machine Learning: An Introduction - Table of Contents
1. Introduction
PART I: FOUNDATIONS
2. Probability: Univariate Models
3. Probability: Multivariate Models
4. Statistics
5. Decision Theory
6. Information Theory
7. Linear Algebra
8. Optimization
PART II: LINEAR MODELS
9. Linear Discriminant Analysis
10. Logistic Regression
11. Linear Regression
12. Generalized Linear Models
PART III: DEEP NEURAL NETWORKS
13. Neural Networks for Tabular Data
14. Neural Networks for Images
15. Neural Networks for Sequences
PART IV: NONPARAMETRIC MODELS
16. Exemplar-Based Methods
17. Kernel Methods
18. Trees, Forests, Bagging, and Boosting
PART V: BEYOND SUPERVISED LEARNING
19. Learning with Fewer Labeled Examples
20. Dimensionality Reduction
21. Clustering
22. Recommender Systems
23. Graph Embeddings
What You Will Learn in Probabilistic Machine Learning: An Introduction
"Probabilistic Machine Learning: An Introduction" by "Kevin P. Murphy" is a clear and practical guide to "probabilistic machine learning". The book introduces fundamental concepts that allow machines to make predictions while reasoning under uncertainty. It explains how to model complex data using "Bayesian methods", "graphical models", and both generative and discriminative approaches. The focus is on building intuition and understanding the mathematical principles behind probabilistic models.
The text covers essential "inference" techniques, including exact and approximate methods, and shows how these tools are applied to real-world problems. Key examples include computer vision, natural language processing, and time-series prediction. The book emphasizes practical implementation while providing the theoretical foundations needed to understand why these models work. Exercises and examples reinforce learning, making complex concepts accessible to both students and practitioners.
Designed for graduate students, researchers, and data scientists, this book is an ideal starting point for anyone new to probabilistic machine learning. By mastering these foundational methods, readers gain the ability to build robust predictive models, reason under uncertainty, and understand the principles underlying modern "predictive modeling". It lays the groundwork for more advanced topics, preparing readers for applications in high-dimensional and structured data scenarios.
Book Details & Specifications
Title:
Probabilistic Machine Learning: An Introduction by Kevin Murphy
Publisher:
The MIT Press
Year:
2022
Pages:
860
Type:
PDF
Language:
English
ISBN-10 #:
0262046822
ISBN-13 #:
978-0262046824
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" expert. 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 has published influential research on probabilistic methods, generative models, and statistical approaches to "artificial intelligence". Murphy was an associate professor at the University of British Columbia and is now a senior "research scientist" at Google. His work focuses on deep probabilistic models, reinforcement learning, and decision-making under uncertainty, helping practitioners build robust, interpretable "models" for real-world problems.
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