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.

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Books under this sub-category (31 books)

An Introduction to Statistical Learning with Applications in R

Post date: 07 Apr 2016
An introduction to statistical learning methods, this book contains a number of R labs with detailed explanations on how to implement the various methods in real life settings.
Publisher: Springer-Verlag GmbH
Publication date: 24 Jun 2014
 
 An Introduction to Statistical Learning with Applications in R

An Introduction to Statistical Learning with Applications in R

Post date: 07 Apr 2016
An introduction to statistical learning methods, this book contains a number of R labs with detailed explanations on how to implement the various methods in real life settings.
Publication date: 24 Jun 2014


[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book
 
[Early Access Version] Model-Based Machine Learning

[Early Access Version] Model-Based Machine Learning

Post date: 15 Dec 2016
This book looks at machine learning from a perspective called model-based machine learning. This viewpoint will guide you towards building successful machine learning solutions without requiring that you master the huge literature on machine learning.
Publication date: 01 Jan 2016
Document Type: Book


A Course in Machine Learning

Post date: 02 Apr 2016
A set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Publication date: 01 Sep 2015
 
A Course in Machine Learning

A Course in Machine Learning

Post date: 02 Apr 2016
A set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Publication date: 01 Sep 2015


A First Encounter with Machine Learning

Post date: 14 Apr 2016
A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book
 
A First Encounter with Machine Learning

A First Encounter with Machine Learning

Post date: 14 Apr 2016
A simple, intuitive introduction into the concepts of machine learning.
Publication date: 21 Apr 2010
Document Type: Book


Applied Data Science

Post date: 29 Jun 2016
Lecture notes for Applied Data Science course at Columbia University. It focuses more on the statistics edge, while also teaching readers some basic programming skill.
Publication date: 31 Dec 2012
 
Applied Data Science

Applied Data Science

Post date: 29 Jun 2016
Lecture notes for Applied Data Science course at Columbia University. It focuses more on the statistics edge, while also teaching readers some basic programming skill.
Publication date: 31 Dec 2012


Azure Machine Learning

Post date: 04 Oct 2016
Introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then deploy those models for consumption as cloud web services.
Publisher: Pearson Education
Publication date: 25 Apr 2015
Document Type: Book
 
Azure Machine Learning

Azure Machine Learning

Post date: 04 Oct 2016
Introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then deploy those models for consumption as cloud web services.
Publication date: 25 Apr 2015
Document Type: Book


Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Post date: 08 Apr 2016
An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.
Publisher: Addison-Wesley Professional
Publication date: 12 Oct 2015
 
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

Post date: 08 Apr 2016
An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.
Publication date: 12 Oct 2015


Bayesian Reasoning and Machine Learning

Post date: 14 Apr 2016
This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided.
Publisher: Cambridge University Press
Publication date: 18 Nov 2015
 
Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning

Post date: 14 Apr 2016
This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided.
Publication date: 18 Nov 2015


Building Machine Learning Systems with Python

Post date: 06 Mar 2017
This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
Publisher: Packt Publishing
Publication date: 26 Jul 2013
Document Type: Book
 
Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

Post date: 06 Mar 2017
This book provides you with an accessible route into Python machine learning, featuring a wealth of real-world examples.
Publication date: 26 Jul 2013
Document Type: Book


Computer Science & Information Technology

Post date: 17 Apr 2017
Proceeding from the Fourth International Conference on Computer Science and Information Technology (CoSIT 2017) Geneva, Switzerland, March 25-26, 2017.
Publisher: AIRCC Publishing Corporation
Publication date: 01 Apr 2017
License: Creative Commons Attribution 4.0 International
Document Type: Proceeding
 
Computer Science & Information Technology

Computer Science & Information Technology

Post date: 17 Apr 2017
Proceeding from the Fourth International Conference on Computer Science and Information Technology (CoSIT 2017) Geneva, Switzerland, March 25-26, 2017.
Publication date: 01 Apr 2017
License: Creative Commons Attribution 4.0 International Document Type: Proceeding


Data-Intensive Text Processing with MapReduce

Post date: 13 Apr 2016
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Publisher: Morgan & Claypool Publishers
Publication date: 30 Apr 2010
 
Data-Intensive Text Processing with MapReduce

Data-Intensive Text Processing with MapReduce

Post date: 13 Apr 2016
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
Publication date: 30 Apr 2010


Deep Learning

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.
Publisher: The MIT Press
Publication date: 18 Nov 2016
Document Type: Textbook
 
Deep Learning

Deep Learning

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.
Publication date: 18 Nov 2016
Document Type: Textbook


Deep Learning Tutorials

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.
Publication date: 29 Mar 2016
 
Deep Learning Tutorials

Deep Learning Tutorials

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.
Publication date: 29 Mar 2016


Foundations of Data Science

Post date: 29 Sep 2016
Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook
 
Foundations of Data Science

Foundations of Data Science

Post date: 29 Sep 2016
Provides the background needed for a modern theoretical course in computer science.
Publication date: 11 Apr 2014
Document Type: Textbook


Gaussian Processes for Machine Learning

Post date: 04 Oct 2007
The 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.
Publisher: The MIT Press
Publication date: 31 Dec 2006
 
Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning

Post date: 04 Oct 2007
The 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.
Publication date: 31 Dec 2006


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