Название: Timeless Algorithms, The Foundational Ideas (MEAP 4)
Автор: Gary Sutton
Издательство: Manning Publications
Год: 2026
Страниц: 715
Язык: английский
Формат: pdf, epub
Размер: 12.3 MB

Understand the enduring algorithms behind modern AI and Data Science.

Timeless Algorithms, The Foundational Ideas explores 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.

Timeless Algorithms, The Foundational Ideas 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

Timeless Algorithms, The Foundational Ideas 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.

about the book
Timeless Algorithms, The Foundational Ideas 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.

To get the most from this book, you’ll want to bring some familiarity with data analysis or programming—whether in Python, R, or SQL—and a working knowledge of basic statistics and probability. If you’ve ever used regression, built a Machine Learning model, or interpreted a dashboard, you already have what you need. This book is designed for readers who want to move beyond running code to truly understanding why their algorithms work and how the principles behind them still guide modern intelligence.

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.

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