Statistical Learning with Math and R: 100 Exercises for Building Logic

Mathematical logic is essential for machine learning and data science, and this textbook approaches these fields by considering math problems and building R programs. It covers essential topics in statistical learning, such as linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning, and provides solutions to 100 exercises.

\n Format: Paperback / softback
\n Length: 217 pages
\n Publication date: 20 October 2020
\n Publisher: Springer Verlag, Singapore
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The most essential skill for machine learning and data science is mathematical logic, which enables one to comprehend their core principles rather than relying solely on knowledge and experience. This textbook takes a unique approach to exploring the essence of machine learning and data science by focusing on math problems and creating R programs.

In the first chapter, a concise introduction to linear algebra is provided, which will serve as a foundation for beginners to delve deeper into the subsequent main chapters. The subsequent chapters cover essential topics in statistical learning, including 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, while also building the corresponding programs.

To enhance the understanding of the material, each chapter is accompanied by proofs and programs in an appendix. Additionally, at the end of each chapter, there are exercises designed to test the reader's knowledge and reinforce the concepts covered. The book is meticulously organized to ensure that the solutions to these exercises are readily accessible by following the contents of each chapter.

This textbook is designed for an undergraduate or graduate course that spans approximately 12 lectures. Its easy-to-follow and self-contained style makes it an ideal resource for independent learning as well. Whether you are a student, researcher, or professional seeking to expand your knowledge in machine learning and data science, this textbook will provide you with a solid foundation and practical insights into these rapidly evolving fields.

\n Weight: 370g\n
Dimension: 155 x 233 x 20 (mm)\n
ISBN-13: 9789811575679\n
Edition number: 1st ed. 2020\n