In this book, we explore the fascinating realm of Gibbs measures and their transformative applications in machine learning. We begin with foundational concepts in probability theory and progress to advanced topics in statistical inference, demonstrating how Gibbs measures can be leveraged to model complex data and enhance prediction accuracy.Through clear explanations and practical examples, readers will learn how to implement Gibbs sampling, Markov Chain Monte Carlo methods, and other probabilistic models in machine learning projects. We also examine the latest research trends in the field, shedding light on how Gibbs measures are shaping the future of artificial intelligence.Whether you're an experienced data scientist or just starting out in machine learning, this book provides the essential knowledge and tools to effectively apply Gibbs measures in developing robust and efficient predictive models. Dive into the world of probabilistic modeling and open new avenues for your data analysis projects.