"All of Statistics: A Concise Course in Statistical Inference" by Larry Wasserman is a textbook designed for graduate students and advanced undergraduates in computer science, mathematics, statistics, and related fields. It provides a comprehensive overview of probability and statistics, covering both classical and modern topics. The book is structured into three parts: probability theory, statistical inference, and applications of statistical inference to specific problems. Part I covers probability theory, focusing on the fundamental concepts and their role in statistical inference. Part II discusses statistical inference, including data mining and machine learning, emphasizing the inverse of probability: given the outcomes, what can we infer about the data-generating process. Part III applies these concepts to specific problems such as regression, graphical models, causation, density estimation, smoothing, classification, and simulation. The book includes advanced topics such as nonparametric regression, bootstrapping, density estimation, and graphical models, which are typically not covered in introductory courses. It avoids tedious calculations in favor of emphasizing concepts and covers nonparametric inference before parametric inference. The book also moves quickly through the material, covering a broad range of topics in a concise manner. The text is written in a clear and intuitive style, with many results stated without proof, and references provided for further reading. It includes R code on the author's website for computational examples, though it is not tied to R. The book also includes a statistics/data mining dictionary to help readers understand the terminology used in the field. The book is suitable for students who want to learn probability and statistics quickly and is designed to provide a solid foundation in basic probability and mathematical statistics, which is essential for students who analyze data or aspire to develop new methods for analyzing data. It emphasizes the importance of understanding basic statistics before using advanced tools like neural nets, boosting, and support vector machines."All of Statistics: A Concise Course in Statistical Inference" by Larry Wasserman is a textbook designed for graduate students and advanced undergraduates in computer science, mathematics, statistics, and related fields. It provides a comprehensive overview of probability and statistics, covering both classical and modern topics. The book is structured into three parts: probability theory, statistical inference, and applications of statistical inference to specific problems. Part I covers probability theory, focusing on the fundamental concepts and their role in statistical inference. Part II discusses statistical inference, including data mining and machine learning, emphasizing the inverse of probability: given the outcomes, what can we infer about the data-generating process. Part III applies these concepts to specific problems such as regression, graphical models, causation, density estimation, smoothing, classification, and simulation. The book includes advanced topics such as nonparametric regression, bootstrapping, density estimation, and graphical models, which are typically not covered in introductory courses. It avoids tedious calculations in favor of emphasizing concepts and covers nonparametric inference before parametric inference. The book also moves quickly through the material, covering a broad range of topics in a concise manner. The text is written in a clear and intuitive style, with many results stated without proof, and references provided for further reading. It includes R code on the author's website for computational examples, though it is not tied to R. The book also includes a statistics/data mining dictionary to help readers understand the terminology used in the field. The book is suitable for students who want to learn probability and statistics quickly and is designed to provide a solid foundation in basic probability and mathematical statistics, which is essential for students who analyze data or aspire to develop new methods for analyzing data. It emphasizes the importance of understanding basic statistics before using advanced tools like neural nets, boosting, and support vector machines.