Fundamentals of Machine Learning for Predictive Data Analytics | Artificial Intelligence | The Elements of Statistical Learning | Python Machine Learning | Probabilistic Graphical Models | Machine Learning | An Introduction to Statistical Learning | Information Theory, Inference and Learning Algorithms
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.
Pricing is shown for items sent to or within the U.S., excluding shipping and tax. Please consult the store to determine exact fees. No warranties are made express or implied about the accuracy, timeliness, merit, or value of the information provided. Information subject to change without notice. isbn.nu is not a bookseller, just an information source.