Probabilistic Machine Learning: Complete Guide (Introduction & Advanced Topics) (Adaptive Computation and Machine Learning Series)

Probabilistic Machine Learning (Bundle)

Authors: Kevin P. Murphy
Publisher: The MIT Press (2023) · Language: English

Books in This Bundle

1. Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning Series)
   ISBN-10: 0262046822 · ISBN-13: 978-0262046824

2. Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning Series)
   ISBN-10: 0262048434 · ISBN-13: 978-0262048439

Overview

This bundle by Kevin P. Murphy provides a comprehensive, probabilistic approach to modern machine learning, covering both foundational concepts and advanced methods. Probabilistic modeling offers a principled framework to handle uncertainty, inference, and decision-making in AI systems, making these texts essential for students, researchers, and practitioners in machine learning and data science.

The first book focuses on fundamentals, while the second book dives into cutting-edge topics, including complex models, approximate inference, and large-scale probabilistic methods. Together, they equip readers with a complete toolkit for understanding, implementing, and innovating probabilistic machine learning systems.

What You’ll Learn

From the Introduction Volume

• Probabilistic reasoning and graphical models

• Supervised and unsupervised learning

• Bayesian inference, conjugacy, and approximate methods

• Decision theory and uncertainty quantification

• Implementation of foundational algorithms

From the Advanced Topics Volume

• Deep probabilistic models and variational inference

• Monte Carlo methods and advanced sampling techniques

• Sequential and structured probabilistic models

• Scalable learning for large datasets

• Integration of probabilistic modeling with modern machine learning pipelines

Why This Bundle Matters

This combined resource is the definitive guide to probabilistic machine learning, blending theory, algorithms, and practical application. The Introduction lays the groundwork for understanding probabilistic reasoning, while Advanced Topics extends this knowledge to state-of-the-art techniques used in AI research and industrial applications.

Together, they provide a full spectrum learning path for mastering probabilistic approaches, from fundamentals to advanced methods, and are indispensable for anyone pursuing research or applied machine learning with a probabilistic perspective.

Who Should Read This Bundle

• Graduate and advanced undergraduate students in machine learning, AI, or statistics.

• Researchers and academics working on probabilistic models.

• Data scientists and AI engineers implementing probabilistic algorithms in practice.

• Machine learning enthusiasts seeking a deep, unified understanding of probabilistic approaches.

Key Topics Covered

• Probabilistic modeling and inference

• Graphical models and Bayesian networks

• Supervised and unsupervised learning

• Approximate inference and Monte Carlo methods

• Deep probabilistic models and variational methods

• Sequential, structured, and hierarchical models

• Scalable algorithms for large datasets

Conclusion

The Probabilistic Machine Learning Bundle by Kevin P. Murphy is a complete, authoritative reference bridging foundational knowledge and advanced methods.

By combining the Introduction and Advanced Topics volumes, readers gain a full understanding of probabilistic machine learning theory and practice, enabling them to analyze, model, and solve complex problems in AI and data-driven fields.