A First Course in Machine Learning, by Mark Girolami and Simon Rogers, offers a clear yet rigorous introduction to the statistical methods underpinning machine learning. The book begins with a detailed exploration of Bayesian analysis in straightforward settings before seamlessly extending into advanced topics such as infinite mixture models, Gaussian processes, and Markov chain Monte Carlo techniques. Throughout, readers are provided with the essential mathematical background in linear algebra, calculus, and probability theory needed for a deep understanding of the field. Designed with both advanced undergraduate and taught master’s courses in mind, this edition skillfully balances theoretical precision with practical insight. It introduces core concepts such as mathematical modeling, inference, and prediction in a manner that is both accessible and thorough. Whether you are new to machine learning or looking to deepen your knowledge, this text is a trusted resource for exploring the dynamic landscape of computer science.
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