Book Reviews
- Too many machine learning books so it's hard to pick the right one. for introduction: Pattern Recognition and ML for theory/algos: Understanding ML for getting started with code: Hands-on ML for the math part: Math for ML for deep learning part: Deep Learning with PythonLink to Tweet
- These two books helped with improving my mathematical understanding of predictive models: 📘 Pattern Recognition and Machine Learning (by Christopher M. Bishop) 📘 The Elements of Statistical Learning (by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie)Link to Tweet
About Book
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.