Description
This book provides a rigorous yet accessible introduction to probability theory and statistical methods that form the mathematical foundation of machine learning. Ethem Alpaydin, a renowned expert in the field, presents core concepts including probability distributions, hypothesis testing, parameter estimation, and Bayesian inference.
The material is carefully structured to help readers understand how statistical principles underpin modern machine learning algorithms. Through clear explanations and practical examples, the book bridges the gap between theoretical mathematics and real-world ML applications. Whether you’re a student entering the field or a practitioner seeking to deepen your mathematical understanding, this MIT Press publication offers essential knowledge for building robust machine learning systems and making informed decisions about model selection and evaluation.
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