Practical discrete mathematics : discover math principles that fuel algorithms for computer science and machine learning with python / Ryan T. White and Archana Tikayat Ray

Includes index.

Cover
pp Intro-Intro --
Title Page --
Copyright and Credits --
About Packt --
Contributors --
Table of Contents --
Preface --
Part I – Basic Concepts of Discrete Math --
Chapter 1: Key Concepts, Notation, Set Theory, Relations, and Functions --
Chapter 2: Formal Logic and Constructing Mathematical Proofs --
Chapter 3: Computing with Base-n Numbers --
Chapter 4: Combinatorics Using SciPy --
Chapter 5: Elements of Discrete Probability --
Part II – Implementing Discrete Mathematics in Data and Computer Science --
Chapter 6: Computational Algorithms in Linear Algebra --
Chapter 7: Computational Requirements for Algorithms --
Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks --
Chapter 9: Searching Data Structures and Finding Shortest Paths --
Part III – Real-World Applications of Discrete Mathematics --
Chapter 10: Regression Analysis with NumPy and Scikit-Learn --
Chapter 11: Web Searches with PageRank --
Chapter 12: Principal Component Analysis with Scikit-Learn --
Index.

Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to another level. You'll learn the language of discrete mathematics and methods crucial to studying and describing objects and algorithms from computer science and machine learning. Complete with real-world examples, this book covers the internal workings of memory and CPUs, analyzes data for useful patterns, and shows you how to solve problems in network routing, web searching, and data science.