Timeless Algorithms: The Seminal Papers

Understand the enduring algorithms behind modern AI and data science. Explore the breakthrough algorithms that power modern AI—including Bayes’ prior and posterior beliefs, Fisher’s estimation and likelihood, Shannon’s information gain, and Breiman’s algorithmic modeling. With clarity and rigor, statistics expert Gary Sutton unpacks each concept and explains its practical relevance. This book explains both the how and the why of the most important data science algorithms. Along with the theory and practical application, you’ll get the fascinating stories behind the discoveries by Bayes, Fisher, Shannon, Bellman, and others. You’ll especially appreciate how author Gary Sutton makes the sometimes-complex seminal papers come to life in rich detail. Timeless Algorithms: The Seminal Papers will help you to: • Diagnose model failures by detecting bias, drift, and overfitting early • Connect tools to theory by linking modern methods to their intellectual roots • Interpret model behavior for both technical and non-technical stakeholders • Balance accuracy and ethics by weighing performance against transparency and fairness • Think probabilistically by applying Bayesian inference, entropy, and expected value • Design trustworthy systems by making deliberate, well-founded choices about data, loss, and structure • Recognize hidden assumptions by uncovering what every model quietly believes about the world • Apply automation tools—such as generative AI and AutoML—while maintaining interpretability and human oversight About the book Timeless Algorithms: The Seminal Papers uses the insights of AI pioneers to help you diagnose failures, recognize hidden assumptions, and reason across the layers of your models and applications. Each chapter connects a common data tool to its seminal mathematics paper, revealing the “hidden stack”—a unique framework that maps the layers of modern intelligence from data to philosophy. With a focus on judgement and ethics, you’ll learn to design trustworthy systems, think probabilistically, and use automation wisely to build intelligent models that are not just effective, but principled. About the reader For data scientists, engineers, statisticians, business analysts, and decision-makers. About the author Gary Sutton is a business intelligence and analytics leader and the author of Statistics Slam Dunk: Statistical analysis with R on real NBA data, and Statistics Every Programmer Needs.