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Inference in Statistical Modelling and Machine Learning出版済み 3-5週間でお届けいたします。
Title: Inference in Statistical Modelling and Machine Learning Subtitle: A Concise Introduction Author: Burridge, James (University of Portsmouth) / Tosh, Nick (University of Galway) Publisher: Cambridge University Press ISBN: 9781009630689 Cover: HARDCOVER Date: 2026年07月 DESCRIPTION 統計モデリングと機械学習における推論：簡潔な入門 統計モデリングと機械学習に関するこの簡潔な入門書は、中核となる概念と、厳選された代表的手法に焦点を合わせています。初歩的な微積分、確率、線形代数のみを必要とする本書によって、読者はすぐに活用できるツールキットが手に入るとともに、より高度な資料を参照する準備が整います。 Statistical modelling and machine learning offer a vast toolbox of inference methods with which to model the world, discover patterns and reach beyond the data to make predictions when the truth is not certain. This concise book provides a clear introduction to those tools and to the core ideas - probabilistic model, likelihood, prior, posterior, overfitting, underfitting, cross-validation - that unify them. Toy and real examples illustrate diverse applications ranging from biomedical data to treasure hunts, while the accompanying datasets and computational notebooks in R and Python encourage hands-on learning. Instructors can benefit from online lecture slides and solutions to all the exercises. Requiring only first-year university-level knowledge of calculus, probability and linear algebra, the book equips students in statistics, data science and machine learning, as well as those in quantitative applied and social science programmes, with the tools and conceptual foundations to explore more advanced techniques. Focuses on core ideas which apply to all inference problems, equipping readers with the tools needed to consult more advanced texts Features an array of appealing case studies, tackled using a wide range of tools but highlighting common core ideas Emphasises ideas first, before discussing the mathematical machinery behind them, allowing readers to understand the core ideas in the simplest and most intuitive way possible Online resources include computational notebooks in R and Python and datasets. Lecture slides and exercise solutions are available for instructors TABLE OF CONTENTS 1. Orientation 2. Supervised learning warm-up 3. Unsupervised learning warm-up 4. Interlude: probability, likelihood and Bayes 5. Probabilistic modelling 6. Frequentist and Bayesian uncertainty 7. Frequentist linear regression 8. Directed graphical models 9. Bayesian linear regression, priors, and regularisation 10. Bayesian methods 11. Classification 12. Unsupervised learning: a deeper dive 13. Neural networks and deep learning 14. Expanding the toolkit A. Probability theory B. Linear algebra C. Jensen's and Gibbs' inequalities
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