Machine Learning
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.
All categoriesBooks under this sub-category (39 books)
Post date: 16 Apr 2016
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
Post date: 16 Apr 2016The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
Post date: 29 Mar 2016
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
Publication date: 29 Mar 2016
Post date: 29 Mar 2016The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano.
Publication date: 29 Mar 2016
Post date: 29 Sep 2016
Provides the background needed for a modern theoretical course in computer science.
Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook
Document Type: Textbook
Post date: 29 Sep 2016Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook
Document Type: Textbook
Foundations of Machine Learning, Second Edition
Post date: 05 Feb 2021A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
Publisher:
The MIT Press
Publication date: 25 Dec 2018
License: The MIT License (MIT)
Document Type: Textbook
Publication date: 25 Dec 2018
License: The MIT License (MIT)
Document Type: Textbook
Foundations of Machine Learning, Second Edition
Post date: 05 Feb 2021A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
Publisher:
The MIT Press
Publication date: 25 Dec 2018
License: The MIT License (MIT) Document Type: Textbook
Publication date: 25 Dec 2018
License: The MIT License (MIT) Document Type: Textbook
Free and Open Machine Learning
Post date: 30 Jul 2020This book describes an open machine learning architecture. Including key aspects that are involved for real business use. It focuses on FOSS machine learning software and open datasets.
Publisher:
The Business Management Support Foundation
Publication date: 04 Jul 2020
License: Creative Commons Attribution-ShareAlike 4.0 International
Document Type: Book
Publication date: 04 Jul 2020
License: Creative Commons Attribution-ShareAlike 4.0 International
Document Type: Book
Free and Open Machine Learning
Post date: 30 Jul 2020This book describes an open machine learning architecture. Including key aspects that are involved for real business use. It focuses on FOSS machine learning software and open datasets.
Publisher:
The Business Management Support Foundation
Publication date: 04 Jul 2020
License: Creative Commons Attribution-ShareAlike 4.0 International Document Type: Book
Publication date: 04 Jul 2020
License: Creative Commons Attribution-ShareAlike 4.0 International Document Type: Book
Gaussian Processes for Machine Learning
Post date: 04 Oct 2007The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. Contains illustrative examples and exercises, and code and datasets are available on the Web.
Gaussian Processes for Machine Learning
Post date: 04 Oct 2007The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. Contains illustrative examples and exercises, and code and datasets are available on the Web.
Introduction to Machine Learning
Post date: 19 Nov 2006This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. It is neither a handbook of practice nor a compendium of theoretical proofs.
Publication date: 01 Jan 2005
Introduction to Machine Learning
Post date: 19 Nov 2006This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. It is neither a handbook of practice nor a compendium of theoretical proofs.
Publication date: 01 Jan 2005
Introduction to Machine Learning
Post date: 02 Apr 2016These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.
Introduction to Machine Learning
Post date: 02 Apr 2016These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.
Introduction to Machine Learning
Post date: 29 May 2016Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Publication date: 23 Apr 2009
Introduction to Machine Learning
Post date: 29 May 2016Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Publication date: 23 Apr 2009
Post date: 22 Feb 2021
This book serves as an introductory text and survey to lifelong learning.
This book serves as an introductory text and survey to lifelong learning.
Post date: 22 Feb 2021This book serves as an introductory text and survey to lifelong learning.
Post date: 21 Apr 2016
A collection of 20 publications on Machine Learning.
A collection of 20 publications on Machine Learning.
Publisher:
I-Tech Education and Publishing
Publication date: 01 Jan 2009
License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
Document Type: Book
Publication date: 01 Jan 2009
License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
Document Type: Book
Post date: 21 Apr 2016A collection of 20 publications on Machine Learning.
Publisher:
I-Tech Education and Publishing
Publication date: 01 Jan 2009
License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported Document Type: Book
Publication date: 01 Jan 2009
License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported Document Type: Book
Machine Learning and Data Mining Lecture Notes
Post date: 05 Aug 2016Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.
Publication date: 06 Feb 2012
Document Type: Lecture Notes
Document Type: Lecture Notes
Machine Learning and Data Mining Lecture Notes
Post date: 05 Aug 2016Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.
Publication date: 06 Feb 2012
Document Type: Lecture Notes
Document Type: Lecture Notes
Post date: 23 Feb 2021
This book will help you or your team making rapid progress while working on a machine learning application.
This book will help you or your team making rapid progress while working on a machine learning application.
Publication date: 01 Jan 2018
Document Type: Book
Document Type: Book
Post date: 23 Feb 2021This book will help you or your team making rapid progress while working on a machine learning application.
Publication date: 01 Jan 2018
Document Type: Book
Document Type: Book
Machine Learning, Neural and Statistical Classification
Post date: 25 Jan 2007The aim of this book is to provide a review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
Machine Learning, Neural and Statistical Classification
Post date: 25 Jan 2007The aim of this book is to provide a review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
Post date: 19 Dec 2016This textbook presents a new approach to numerical analysis for modern computer scientists and introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
Post date: 19 Dec 2016This textbook presents a new approach to numerical analysis for modern computer scientists and introduces numerical modeling and algorithmic design from a practical standpoint and provides insight into the theoretical tools needed to support these skills.
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