"**All of Statistics: A Concise Course in Statistical Inference**" by Larry Wasserman is a comprehensive textbook designed for graduate students and advanced undergraduates in computer science, mathematics, statistics, and related fields. The book covers a broad range of topics in probability and statistics, including modern concepts such as nonparametric curve estimation, bootstrapping, and classification. It is suitable for those who want to learn probability and statistics quickly and understand the foundational concepts without delving into tedious and uninspiring topics. The book is divided into three main parts: 1. **Probability**: Focuses on the formal language of uncertainty, including sample spaces, events, probability, random variables, expectation, inequalities, and convergence of random variables. 2. **Statistical Inference**: Covers models, statistical inference, and learning, including parametric and nonparametric models, point estimation, confidence sets, hypothesis testing, and Bayesian inference. 3. **Statistical Models and Methods**: Applies the concepts from Part II to specific problems such as regression, graphical models, causation, density estimation, smoothing, classification, and simulation. It also includes a chapter on stochastic processes and simulation methods. The author emphasizes the importance of a strong foundation in basic probability and mathematical statistics for students who analyze data or develop new methods for data analysis. The book is not tied to any specific computing language and includes R code files on the author's website for practical computing exercises. The book is intended to bridge the gap between statistical theory and its practical applications in data mining and machine learning."**All of Statistics: A Concise Course in Statistical Inference**" by Larry Wasserman is a comprehensive textbook designed for graduate students and advanced undergraduates in computer science, mathematics, statistics, and related fields. The book covers a broad range of topics in probability and statistics, including modern concepts such as nonparametric curve estimation, bootstrapping, and classification. It is suitable for those who want to learn probability and statistics quickly and understand the foundational concepts without delving into tedious and uninspiring topics. The book is divided into three main parts: 1. **Probability**: Focuses on the formal language of uncertainty, including sample spaces, events, probability, random variables, expectation, inequalities, and convergence of random variables. 2. **Statistical Inference**: Covers models, statistical inference, and learning, including parametric and nonparametric models, point estimation, confidence sets, hypothesis testing, and Bayesian inference. 3. **Statistical Models and Methods**: Applies the concepts from Part II to specific problems such as regression, graphical models, causation, density estimation, smoothing, classification, and simulation. It also includes a chapter on stochastic processes and simulation methods. The author emphasizes the importance of a strong foundation in basic probability and mathematical statistics for students who analyze data or develop new methods for data analysis. The book is not tied to any specific computing language and includes R code files on the author's website for practical computing exercises. The book is intended to bridge the gap between statistical theory and its practical applications in data mining and machine learning.