Books

Not just for mathematicians and detectives! Learn the basics of logic and its many applications, including advanced testing, constraint solving, function composition, and code proofs. Over 50 exercises and 20 sample programs are included. No prior math background required!

If you are able to contribute, it will go to support OpenIntro (not the author), a US-based nonprofit working to spread open materials, e.g. by providing desk copies to instructors considering this text. This listing is in collaboration with the textbook's author, Jim Hefferon. Linear Algebra's official websitePaperbacks are $22openintro.org

This book supplements the DM for CS Specialization at Coursera and contains many interactive puzzles, autograded quizzes, and code snippets. They are intended to help you to discover important ideas in discrete mathematics on your own. By purchasing the book, you will get all updates of the book free of charge when they are released.

Leanpub revenue supports OpenIntro. OpenIntro is a US-based nonprofit that provides textbook services to help increase adoption of OER textbooks and save students money. These textbook services are fully financed by Leanpub contributions on supported books. APEX Calculus website: apexcalculus.comOpenIntro website: openintro.org

A powerful new book on Conformal Prediction by bestselling author and machine learning expert Valery Manokhin, bridging theory and real-world machine learning. Discover how to quantify uncertainty with statistical guarantees—across deep learning, time series, forecasting, and more. Preorder now before the price goes up.

A complete foundation for Statistics, also serving as a foundation for Data Science, that introduces inference using randomization and simulation while covering traditional methods. Leanpub revenue supports OpenIntro, so we can provide free desk copies to teachers interested in using our books in the classroom. More resources: openintro.org.

Stop guessing which machine learning algorithm to use. This book provides clear mathematical explanations, decision frameworks, and real-world examples to help you select, implement, and evaluate models correctly from data to deployment.

The essentials of making predictions using supervised regression and classification for tabular data. Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, TabICL

How should an AI system make decisions when information is incomplete?How can machines quantify uncertainty instead of merely producing predictions?How can intelligent systems continuously update their beliefs as new evidence emerges?The answer lies in Bayesian Mathematics.In Bayesian Mathematics for AI Decision Making, Anshuman Mishra explores the powerful framework that enables modern AI systems to reason probabilistically, model uncertainty, and make rational decisions in complex environments.From Bayesian inference and probabilistic programming to uncertainty-aware deep learning, reinforcement learning, healthcare diagnostics, robotics, and financial forecasting, this book reveals how Bayesian thinking is shaping the next generation of Artificial Intelligence.Learn how uncertainty becomes knowledge—and how probability becomes intelligence.

Artificial Intelligence is not only about neural networks and transformers. Behind many of the world's most influential AI systems lies a powerful probabilistic framework known as the Hidden Markov Model (HMM).From speech recognition and natural language processing to robotics, cybersecurity, finance, and bioinformatics, HMMs remain one of the most important sequence modeling techniques ever developed.This book takes readers on a complete journey through Markov Chains, probabilistic reasoning, Hidden Markov Models, Forward-Backward algorithms, Viterbi decoding, Baum-Welch training, and advanced HMM architectures.Designed for students, researchers, and AI professionals, the book combines rigorous mathematics with practical applications, making complex concepts accessible and immediately useful.If you want to truly understand how intelligent systems model uncertainty, learn from temporal patterns, and reason about hidden information, this book provides the foundation.

Can Hidden Markov Models predict financial market regimes?How are genes discovered from DNA sequences?How do robots determine their location in uncertain environments?Can probabilistic AI still compete with Transformers and Deep Learning?Volume-3 of Hidden Markov Models and AI answers these questions through real-world applications, Python implementations, industrial case studies, advanced projects, and future AI research.Explore how Hidden Markov Models are used in bioinformatics, cybersecurity, finance, robotics, autonomous systems, anomaly detection, and scientific discovery. Learn to build HMM systems from scratch, work with professional AI libraries, and understand the evolving relationship between probabilistic models and deep learning.This volume is designed for readers who want to move beyond theory and develop practical expertise in modern sequential artificial intelligence.

How do voice assistants understand speech?How does a chatbot track conversation context?How can machines identify speakers, translate languages, recognize named entities, and process sequential information?The answer lies in sequence modeling.In Volume-2 of Hidden Markov Models and AI, readers move beyond theory into practical applications of Hidden Markov Models in speech recognition, natural language processing, machine translation, speaker verification, conversational AI, and intelligent decision-making systems.Learn how modern AI systems transform speech signals and language sequences into meaningful intelligence using probabilistic models that continue to influence today's most advanced technologies.Whether you are an AI student, NLP researcher, speech engineer, or machine learning professional, this volume provides the practical knowledge required to master sequential learning systems.

In Multivariate Analysis – The Simplest Guide in the Universe, guides you through the building blocks of multivariate analysis towards discovering the relationships within your data.Here, you’ll discover the most used multivariate analysis tests – and learn how to choose them correctly.