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  1. Bog
  2. Matematikkens fundament
  3. Matematisk logik

Statistical Learning with Math and Python

- 100 Exercises for Building Logic
Af: Joe Suzuki Engelsk Paperback

Statistical Learning with Math and Python

- 100 Exercises for Building Logic
Af: Joe Suzuki Engelsk Paperback
Tjek vores konkurrenters priser
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 machine learning and data science by considering math problems and building Python programs.

As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: 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 and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, 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 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

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Tjek vores konkurrenters priser
Normalpris
kr 335
Fragt: 39 kr
6 - 8 hverdage
20 kr
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God 4 anmeldelser på
Tjek vores konkurrenters priser
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 machine learning and data science by considering math problems and building Python programs.

As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: 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 and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, 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 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 256
ISBN-13: 9789811578762
Indbinding: Paperback
Udgave:
ISBN-10: 9811578761
Kategori: Matematisk logik
Udg. Dato: 4 aug 2021
Længde: 18mm
Bredde: 234mm
Højde: 155mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 4 aug 2021
Forfatter(e): Joe Suzuki
Forfatter(e) Joe Suzuki

Kategori Matematisk logik

ISBN-13 9789811578762

Sprog Engelsk

Indbinding Paperback

Sider 256

Udgave

Længde 18mm

Bredde 234mm

Højde 155mm

Udg. Dato 4 aug 2021

Oplagsdato 4 aug 2021

Forlag Springer Verlag, Singapore

Kategori sammenhænge