Focusing on mutual information (MI) estimation methods, the book delves into innovative approaches for learning forest structures from multivariate data and conducting independent component analysis. It defines MI in the context of information theory, highlighting its role in measuring dependencies between random variables. The author emphasizes the significance of consistent estimations that satisfy independence testing, a combination not previously achieved in existing methods. This work is crucial for applications in machine learning, statistical analysis, and various scientific fields.
Focusing on mathematical logic, this comprehensive textbook delves into the widely applicable information criterion (WAIC) and the Bayesian information criterion (WBIC). It offers a blend of theoretical insights and practical programming experience in Python and Stan, making it ideal for data scientists and researchers. Readers will enhance their model selection skills and explore the latest advancements in Bayesian statistics, all while gaining a solid understanding of Watanabe Bayesian Theory.
Focusing on mathematical logic, this textbook delves into the widely applicable information criterion (WAIC) and the Bayesian information criterion (WBIC), providing a thorough understanding of model selection in machine learning and data science. It presents relevant mathematical problems alongside practical programming experience in R and Stan. Ideal for data scientists and researchers, the guide offers insights into the latest developments in Bayesian statistics, ensuring readers gain a solid grasp of Watanabe Bayesian Theory.
Focusing on the fundamentals of kernel methods, this textbook emphasizes the importance of mathematical logic in machine learning and data science. It integrates relevant mathematical problems with practical programming in Python, providing a comprehensive understanding of the concepts rather than relying solely on prior knowledge or experience.
Focusing on the foundational role of mathematical logic, this textbook delves into sparse estimation, emphasizing its significance in machine learning and data science. It presents complex mathematical problems and guides readers in developing Python programs to tackle these challenges. This approach aims to deepen understanding of the principles underlying data analysis, making it a valuable resource for both students and professionals looking to enhance their skills in this critical area.
This textbook approaches the essence of machine learning and data science by
considering math problems and building Python programs. As the preliminary
part, Chapter 1 provides a concise introduction to linear algebra, which will
help novices read further to the following main chapters.
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
