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 (28 books)

An Introduction to Statistical Learning with Applications in R

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.

An Introduction to Statistical Learning with Applications in R

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.

[Early Access Version] Model-Based Machine Learning

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.

[Early Access Version] Model-Based Machine Learning

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.

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.).

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.).

A First Encounter with Machine Learning

A simple, intuitive introduction into the concepts of machine learning.

A First Encounter with Machine Learning

A simple, intuitive introduction into the concepts of machine learning.

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.

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.

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.

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.

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view, using Python.

Bayesian Reasoning and Machine Learning

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.

Bayesian Reasoning and Machine Learning

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.

Data-Intensive Text Processing with MapReduce

This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

Data-Intensive Text Processing with MapReduce

This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

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.

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.

Provides the background needed for a modern theoretical course in computer science.

Provides the background needed for a modern theoretical course in computer science.

Gaussian Processes for Machine Learning

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.

Gaussian Processes for Machine Learning

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.

Introduction to Machine Learning

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.

Introduction to Machine Learning

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.

Introduction to Machine Learning

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.

Introduction to Machine Learning

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.

Book Categories

Computer Science
15
Introduction to Computer Science
32
Introduction to Computer Programming
52
Algorithms and Data Structures
24
Artificial Intelligence
24
Computer Vision
28
Machine Learning
6
Neural Networks
22
Game Development and Multimedia
25
Data Communication and Networks
5
Coding Theory
14
Computer Security
8
Information Security
34
Cryptography
3
Information Theory
17
Computer Organization and Architecture
22
Operating Systems
1
Image Processing
10
Parallel Computing
4
Concurrent Programming
20
Relational Database
3
Document-oriented Database
13
Data Mining
16
Big Data
17
Data Science
23
Digital Libraries
22
Compiler Design and Construction
26
Functional Programming
11
Logic Programming
26
Object Oriented Programming
21
Formal Methods
69
Software Engineering
3
Agile Software Development
7
Information Systems
5
Geographic Information System (GIS)

Mathematics
66
Mathematics
13
Algebra
1
Abstract Algebra
27
Linear Algebra
3
Number Theory
8
Numerical Methods
2
Precalculus
10
Calculus
2
Differential Equations
5
Category Theory
10
Proofs
19
Discrete Mathematics
24
Theory of Computation
14
Graph Theory
2
Real Analysis
1
Complex Analysis
14
Probability
43
Statistics
7
Game Theory
5
Queueing Theory
13
Operations Research
16
Computer Aided Mathematics

Supporting Fields
19
Web Design and Development
1
Mobile App Design and Development
28
System Administration
2
Cloud Computing
9
Electric Circuits
6
Embedded System
26
Signal Processing
4
Network Science
3
Project Management

Operating System
Programming/Scripting
6
Ada
13
Assembly
34
C / C++
8
Common Lisp
2
Forth
35
Java
12
JavaScript
1
Lua
15
Microsoft .NET
1
Rexx
12
Perl
6
PHP
66
Python
12
R
1
Rebol
13
Ruby
2
Scheme
3
Tcl/Tk

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