Algorithms

Algorithms are the lifeblood of computer science. They are the machines that proofs build and the music that programs play. Their history is as old as mathematics itself. This textbook is a wide-ranging, idiosyncratic treatise on the design and analysis of algorithms, covering several fundamental techniques, with an emphasis on intuition and the problem-solving process. The book includes important classical examples, hundreds of battle-tested exercises, far too many historical digressions, and exaclty four typos. Jeff Erickson is a computer science professor at the University of Illinois, Urbana-Champaign; this book is based on algorithms classes he has taught there since 1998.

• Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming
• Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures (Volume 2)
• Machine Learning: An Applied Mathematics Introduction
• Daily Coding Problem: Get exceptionally good at coding interviews by solving one problem every day
• Algorithms
• Algorithms Illuminated: Part 1: The Basics
• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
• Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
• Operating Systems: Three Easy Pieces
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems