The Mathematics of Machine Learning : : Lectures on Supervised Methods and Beyond / / Maria Han Veiga, François Gaston Ged.
This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detai...
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Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2024] ©2024 |
Year of Publication: | 2024 |
Language: | English |
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Physical Description: | 1 online resource (X, 200 p.) |
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Han Veiga, Maria, author. aut http://id.loc.gov/vocabulary/relators/aut The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / Maria Han Veiga, François Gaston Ged. Berlin ; Boston : De Gruyter, [2024] ©2024 1 online resource (X, 200 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda De Gruyter Textbook Frontmatter -- Preface -- Contents -- Part I: Introduction and preliminaries -- 1 Introduction to machine learning -- 2 Probability review -- 3 Optimization -- Part II: Supervised learning -- 4 Statistical learning theory -- 5 Linear models -- 6 Kernel methods -- 7 Gaussian processes -- 8 Deep learning -- 9 Ensemble methods -- Part III: Beyond supervised learning -- 10 Topics in unsupervised learning -- 11 Reinforcement learning -- Bibliography -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field. Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 02. Jun 2024) Kernel-Methoden. Neuronale Netze. Statistisches Lernen. überwachtes Lernen. MATHEMATICS / Applied. bisacsh Complex analysis, analytic functions, complex integration, complex variables,. Gaston Ged, François, author. aut http://id.loc.gov/vocabulary/relators/aut EPUB 9783111289816 print 9783111288475 https://doi.org/10.1515/9783111288994 https://www.degruyter.com/isbn/9783111288994 Cover https://www.degruyter.com/document/cover/isbn/9783111288994/original |
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English |
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eBook |
author |
Han Veiga, Maria, Han Veiga, Maria, Gaston Ged, François, |
spellingShingle |
Han Veiga, Maria, Han Veiga, Maria, Gaston Ged, François, The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / De Gruyter Textbook Frontmatter -- Preface -- Contents -- Part I: Introduction and preliminaries -- 1 Introduction to machine learning -- 2 Probability review -- 3 Optimization -- Part II: Supervised learning -- 4 Statistical learning theory -- 5 Linear models -- 6 Kernel methods -- 7 Gaussian processes -- 8 Deep learning -- 9 Ensemble methods -- Part III: Beyond supervised learning -- 10 Topics in unsupervised learning -- 11 Reinforcement learning -- Bibliography -- Index |
author_facet |
Han Veiga, Maria, Han Veiga, Maria, Gaston Ged, François, Gaston Ged, François, Gaston Ged, François, |
author_variant |
v m h vm vmh v m h vm vmh g f g gf gfg |
author_role |
VerfasserIn VerfasserIn VerfasserIn |
author2 |
Gaston Ged, François, Gaston Ged, François, |
author2_variant |
g f g gf gfg |
author2_role |
VerfasserIn VerfasserIn |
author_sort |
Han Veiga, Maria, |
title |
The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / |
title_sub |
Lectures on Supervised Methods and Beyond / |
title_full |
The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / Maria Han Veiga, François Gaston Ged. |
title_fullStr |
The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / Maria Han Veiga, François Gaston Ged. |
title_full_unstemmed |
The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / Maria Han Veiga, François Gaston Ged. |
title_auth |
The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / |
title_alt |
Frontmatter -- Preface -- Contents -- Part I: Introduction and preliminaries -- 1 Introduction to machine learning -- 2 Probability review -- 3 Optimization -- Part II: Supervised learning -- 4 Statistical learning theory -- 5 Linear models -- 6 Kernel methods -- 7 Gaussian processes -- 8 Deep learning -- 9 Ensemble methods -- Part III: Beyond supervised learning -- 10 Topics in unsupervised learning -- 11 Reinforcement learning -- Bibliography -- Index |
title_new |
The Mathematics of Machine Learning : |
title_sort |
the mathematics of machine learning : lectures on supervised methods and beyond / |
series |
De Gruyter Textbook |
series2 |
De Gruyter Textbook |
publisher |
De Gruyter, |
publishDate |
2024 |
physical |
1 online resource (X, 200 p.) Issued also in print. |
contents |
Frontmatter -- Preface -- Contents -- Part I: Introduction and preliminaries -- 1 Introduction to machine learning -- 2 Probability review -- 3 Optimization -- Part II: Supervised learning -- 4 Statistical learning theory -- 5 Linear models -- 6 Kernel methods -- 7 Gaussian processes -- 8 Deep learning -- 9 Ensemble methods -- Part III: Beyond supervised learning -- 10 Topics in unsupervised learning -- 11 Reinforcement learning -- Bibliography -- Index |
isbn |
9783111288994 9783111289816 9783111288475 |
url |
https://doi.org/10.1515/9783111288994 https://www.degruyter.com/isbn/9783111288994 https://www.degruyter.com/document/cover/isbn/9783111288994/original |
illustrated |
Not Illustrated |
doi_str_mv |
10.1515/9783111288994 |
work_keys_str_mv |
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status_str |
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ids_txt_mv |
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The Mathematics of Machine Learning : Lectures on Supervised Methods and Beyond / |
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