"Machine Learning: A Probabilistic Perspective"

"Machine Learning: A Probabilistic Perspective" explores how computers can learn from data using probability. Instead of strict rules, it models uncertainty to make predictions and decisions, accounting for real-world variability. By treating data and outcomes as random variables, it enables systems to update their beliefs as new information arrives, improving accuracy over time. This approach provides a flexible, rigorous framework for understanding and developing machine learning algorithms, making them more robust and capable of handling complex, uncertain environments.