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
