Pattern Recognition and Machine Learning

Comprehensive textbook formalizing machine learning completely from a Bayesian graphical modeling perspective.

qualified guide for implementing exact Bayesian inference, graphical models, and Expectation-Maximization algorithms.

Assumptions

• Probabilistic framing with explicit priors and posteriors provides coherent treatment of uncertainty

Limitations

• Deep neural networks are treated from a pre-GPU perspective before AlexNet