Pattern Recognition and Machine Learning (Information Science and Statistics)

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Deep Learning (Adaptive Computation and Machine Learning series)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• Deep Learning with Python
• Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming
• Artificial Intelligence: A Modern Approach