Sparse Estimation with Math and R:100 Exercises for Building Logic

Joe Suzuki

Informasi Umum

Kode

22.21.1817

Klasifikasi

005.131 - Matlab

Jenis

Buku - Elektronik (E-Book)

Subjek

Mathematical And Computational Engineering, Mathematical Logic,

No. Rak

Dilihat

291 kali

Informasi Lainnya

Abstraksi

This book is a sequel and extension to the book “Business Process Management Cases", published in its first edition by Springer in 2018. It adds 22 new cases for practitioners and educators to showcase and study Business Process Management (BPM). The BPM cases collection is dedicated to providing a contemporary and comprehensive, industry-agnostic insight into the realities of BPM. In particular it focuses on the lessons that only authentic cases can provide. The experiences documented cover both, the positive impact of deploying BPM as well as the lessons learnt from failed attempts. Each case takes a holistic approach and by doing so, each chapter recognizes that BPM in practice is a multidimensional endeavor covering strategy to operations, systems and infrastructure, governance and culture, models and running processes.

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 R 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 Python (https://www.springer.com/gp/book/9789811578762)
Sparse Estimation with Math and Python