Sparse Estimation with Math and Python: 100 Exercises for Building Logic
暫譯: 數學與 Python 的稀疏估計：邏輯建構的 100 道練習題
Joe Suzuki
相關主題
商品描述
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 sparse estimation by considering math problems and building Python programs.
Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that 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 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.
This book is one of a series of textbooks in machine learning by the same Author. Other titles are:
- Statistical Learning with Math and R (https: //www.springer.com/gp/book/9789811575679)
- Statistical Learning with Math and Pyth (https: //www.springer.com/gp/book/9789811578762)
- Sparse Estimation with Math and R
商品描述(中文翻譯)
機器學習和數據科學最重要的能力是數學邏輯，以理解其本質，而非僅僅依賴知識和經驗。本教科書通過考慮數學問題並構建 Python 程式來探討稀疏估計的本質。
每一章介紹稀疏性的概念，並提供程序，隨後是數學推導和執行範例的源程式。為了最大化讀者對稀疏性的洞察，幾乎所有命題都提供數學證明，且程式描述不依賴任何套件。本書經過精心組織，提供每章練習題的解答，讓讀者可以通過簡單跟隨每章內容來解決總共 100 道練習題。
本教科書適合用於約 15 堂課（每堂 90 分鐘）的本科或研究生課程。以易於理解且自成一體的風格撰寫，這本書也非常適合數據科學家、機器學習工程師和對線性回歸、廣義線性套索、群組套索、融合套索、圖形模型、矩陣分解和多變量分析感興趣的研究人員進行獨立學習。
本書是同一作者在機器學習領域的一系列教科書之一。其他書名包括：
- Statistical Learning with Math and R (https: //www.springer.com/gp/book/9789811575679)
- Statistical Learning with Math and Python (https: //www.springer.com/gp/book/9789811578762)
- Sparse Estimation with Math and R
作者簡介
Joe Suzuki is a professor of statistics at Osaka University, Japan. He has published more than 100 papers on graphical models and information theory.
作者簡介(中文翻譯)
鈴木喬是日本大阪大學的統計學教授。他在圖形模型和信息理論方面發表了超過100篇論文。
