An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty.

An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.

• Covers generation of high dimensional outputs, such as images, text, and graphs 
• Discusses methods for discovering insights about data, based on latent variable models 
• Considers training and testing under different distributions
• Explores how to use probabilistic models and inference for causal inference and decision making
• Features online Python code accompaniment 

Table of Contents

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 the Author

Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.