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  • 1
    Online Resource
    Online Resource
    Cham :Springer Nature Switzerland :
    UID:
    almafu_9961855532002883
    Format: 1 online resource (298 pages)
    Edition: 1st ed. 2025.
    ISBN: 9783031302763 , 3031302761
    Series Statement: Statistics and Computing,
    Content: This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
    Note: Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.
    Additional Edition: ISBN 9783031302756
    Additional Edition: ISBN 3031302753
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_1918988218
    Format: 1 Online-Ressource (XIV, 282 Seiten)
    ISBN: 9783031302763
    Series Statement: Statistics and computing
    Content: This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.
    Note: Part I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning.
    Additional Edition: ISBN 9783031302756
    Additional Edition: ISBN 9783031302770
    Additional Edition: ISBN 9783031302787
    Additional Edition: Erscheint auch als Druck-Ausgabe Lederer, Johannes, 1984 - A first course in statistical learning Cham : Springer, 2025 ISBN 9783031302756
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    UID:
    b3kat_BV050182997
    Format: xiv, 282 Seiten , Illustrationen
    ISBN: 9783031302756
    Series Statement: Statistics and computing
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-031-30276-3
    Language: English
    Keywords: Wahrscheinlichkeitstheorie ; Statistisches Lernen ; Datenanalyse ; Deep Learning ; Python
    Library Location Call Number Volume/Issue/Year Availability
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