Overview
- Equips readers with the logic required for machine learning and data science
- Provides in-depth understanding of source programs
- Written in an easy-to-follow and self-contained style
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Table of contents (7 chapters)
Keywords
About this book
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
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Sparse Estimation with Math and Python
Book Subtitle: 100 Exercises for Building Logic
Authors: Joe Suzuki
DOI: https://doi.org/10.1007/978-981-16-1438-5
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
Softcover ISBN: 978-981-16-1437-8Published: 31 October 2021
eBook ISBN: 978-981-16-1438-5Published: 30 October 2021
Edition Number: 1
Number of Pages: X, 246
Number of Illustrations: 8 b/w illustrations, 46 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Data Structures and Information Theory, Statistics, general