Topic: Nature & Maths

Popular with and respected by instructors and students interested in a modeling approach, graphing, or graphing calculators, this book incorporates the benefits of technology and the philosophy of the reform...

The Fourth Edition of Yoshiwara and Yoshiwara's MODELING, FUNCTIONS, AND GRAPHS: ALGEBRA FOR COLLEGE STUDENTS includes content found in a typical algebra course, along with introductions to curve-fitting and display...

Open Resources for Community College Algebra (ORCCA) is an open-source, openly licensed textbook package (eBook, print, and online homework) for basic and intermediate algebra. At Portland Community College, Part 1...

This text is intended as a treatise for a rigorous course introducing the elements of integration theory on the real line. All of the important features of the Riemann integral,...

An elementary introduction to integration theory on the real line. This is at the level of an honor's course in calculus or a first undergraduate level real analysis course. In...

This is the second edition of the text Elementary Real Analysis originally published by Prentice Hall (Pearson) in 2001. Additional elementary material designated as enrichment can be included for students with minimal...

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive,...

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine...

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual...

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from...

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for...

The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI...