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 categories

Books under this sub-category (31 books)

Introduction to Machine Learning

Post date: 19 Nov 2006
This 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

Introduction to Machine Learning

Post date: 19 Nov 2006
This 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 2016
These 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.
Publisher: Cambridge University Press
Publication date: 01 Oct 2010
 
Introduction to Machine Learning

Introduction to Machine Learning

Post date: 02 Apr 2016
These 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.
Publisher: Cambridge University Press
Publication date: 01 Oct 2010


Introduction to Machine Learning

Post date: 29 May 2016
Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Publication date: 23 Apr 2009
 
Introduction to Machine Learning

Introduction to Machine Learning

Post date: 29 May 2016
Class notes of Machine Learning course given at the Hebrew University of Jerusalem.
Publication date: 23 Apr 2009


Learning Deep Architectures for AI

Post date: 30 Nov 2016
This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models.
Publication date: 31 Dec 2009
Document Type: Paper
 
Learning Deep Architectures for AI

Learning Deep Architectures for AI

Post date: 30 Nov 2016
This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models.
Publication date: 31 Dec 2009
Document Type: Paper


Machine Learning

Post date: 21 Apr 2016
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
 
Machine Learning

Machine Learning

Post date: 21 Apr 2016
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


Machine Learning and Data Mining Lecture Notes

Post date: 05 Aug 2016
Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.
Publication date: 06 Feb 2012
Document Type: Lecture Notes
 
Machine Learning and Data Mining Lecture Notes

Machine Learning and Data Mining Lecture Notes

Post date: 05 Aug 2016
Lecture notes for CSC 411 Machine Learning and Data Mining course at the University of Toronto.
Publication date: 06 Feb 2012
Document Type: Lecture Notes


Machine Learning, Neural and Statistical Classification

Post date: 25 Jan 2007
The 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.
Publisher: Ellis Horwood
Publication date: 17 Feb 1994
 
Machine Learning, Neural and Statistical Classification

Machine Learning, Neural and Statistical Classification

Post date: 25 Jan 2007
The 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.
Publisher: Ellis Horwood
Publication date: 17 Feb 1994


Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Post date: 19 Dec 2016
This 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.
Publisher: A K Peters, Ltd.
Publication date: 13 Jul 2015
Document Type: Textbook
 
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics

Post date: 19 Dec 2016
This 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.
Publisher: A K Peters, Ltd.
Publication date: 13 Jul 2015
Document Type: Textbook


Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

Post date: 03 May 2017
A hands-on guide to understanding the nature of your data and turn it into insight. Presents a detailed exploration of the current work in data analysis through self-contained projects.
Publisher: Packt Publishing
Publication date: 22 Oct 2013
Document Type: Book
 
Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools

Post date: 03 May 2017
A hands-on guide to understanding the nature of your data and turn it into insight. Presents a detailed exploration of the current work in data analysis through self-contained projects.
Publisher: Packt Publishing
Publication date: 22 Oct 2013
Document Type: Book


Probabilistic Models in the Study of Language

Post date: 30 Nov 2016
This textbook covers the topic of using probabilistic models in scientific work on language ranging from experimental data analysis to corpus work to cognitive modeling.
Publication date: 06 Nov 2012
Document Type: Textbook
 
Probabilistic Models in the Study of Language

Probabilistic Models in the Study of Language

Post date: 30 Nov 2016
This textbook covers the topic of using probabilistic models in scientific work on language ranging from experimental data analysis to corpus work to cognitive modeling.
Publication date: 06 Nov 2012
Document Type: Textbook


Reinforcement Learning

Post date: 21 Apr 2016
The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields.
 
Reinforcement Learning

Reinforcement Learning

Post date: 21 Apr 2016
The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields.

Reinforcement Learning: An Introduction, Second Edition (Draft)

Post date: 09 Jan 2017
This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.
Publisher: The MIT Press
Publication date: 30 Sep 2016
Document Type: Textbook
 
Reinforcement Learning: An Introduction, Second Edition (Draft)

Reinforcement Learning: An Introduction, Second Edition (Draft)

Post date: 09 Jan 2017
This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.
Publisher: The MIT Press
Publication date: 30 Sep 2016
Document Type: Textbook


Statistical Foundations of Machine Learning

Post date: 20 Apr 2016
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data.
Publisher: OTexts
Publication date: 01 Jan 2016
 
Statistical Foundations of Machine Learning

Statistical Foundations of Machine Learning

Post date: 20 Apr 2016
This handbook aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data.
Publisher: OTexts
Publication date: 01 Jan 2016


The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

Post date: 08 Apr 2016
This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.
Publisher: Springer-Verlag GmbH
Publication date: 01 Dec 2015
Document Type: Textbook
 
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

Post date: 08 Apr 2016
This book descibes the important ideas of data mining, machine learning, and bioinformatics in a common conceptual framework. Topics include neural networks, support vector machines, classification trees and boosting.
Publisher: Springer-Verlag GmbH
Publication date: 01 Dec 2015
Document Type: Textbook


Theory and Applications for Advanced Text Mining

Post date: 13 Jun 2016
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.
Publisher: InTech
Publication date: 21 Nov 2012
License: Creative Commons Attribution 3.0 Unported
 
Theory and Applications for Advanced Text Mining

Theory and Applications for Advanced Text Mining

Post date: 13 Jun 2016
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.
Publisher: InTech
Publication date: 21 Nov 2012
License: Creative Commons Attribution 3.0 Unported


Book Categories
Sponsors
Icons8, a free icon pack