Machine Learning – Referências

Esse artigo é sobre Machine Learning, links relevantes e plataformas interessantes.

Nesse estudo, apresento alguns recursos que acredito sejam relevantes em Machine Learning. O foco foi no contexto geral sobre a área e plataformas relacionadas com linguagens de programação mais conhecidas.

Última Atualização: 17/04/2013 (publicação)

Conteúdo:

Referência
Software

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Referência

Machine Learning
http://en.wikipedia.org/wiki/Machine_learning
Machine learning, a branch of artificial intelligence, is about the construction and study of systems that can learn from data. The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.

ACM Transactions on Intelligent Systems and Technology
http://tist.acm.org/
ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a new scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world.

The Journal of Machine Learning Research
http://jmlr.org/
The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.

Course | Machine Learning by Stanford University
http://www.youtube.com/playlist?list=PLA89DCFA6ADACE599
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department.

Machine Learning by mathematicalmonk’s channel
http://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

http://research.yahoo.com/Machine_Learning
http://research.google.com/pubs/MachineLearning.html
http://research.google.com/pubs/ArtificialIntelligenceandMachineLearning.html
http://research.microsoft.com/en-us/about/our-research/machine-learning.aspx

Kaggle
http://www.kaggle.com/
Kaggle is the leading platform for predictive modeling competitions. Companies, governments and researchers present datasets and problems – the world’s best data scientists then compete to produce the best solutions. At the end of a competition, the competition host pays prize money in exchange for the intellectual property behind the winning model.

Machine Learning Open Source Software
http://jmlr.org/mloss
To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing.
http://mloss.org/
Forum for open source software in machine learning.

Machine Learning | Coursera
https://www.coursera.org/course/ml
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

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Software

http://en.wikipedia.org/wiki/Category:Data_mining_and_machine_learning_software

GNU/Linux AI & Alife HOWTO
http://www.tldp.org/HOWTO/AI-Alife-HOWTO-7.html
Libraries or frameworks used for writing machine learning systems.

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(Java)

Apache Mahout
http://mahout.apache.org/
http://en.wikipedia.org/wiki/Apache_Mahout
Mahout’s goal is to build scalable machine learning libraries. The core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm (However Mahout do not restrict contributions to Hadoop based implementations).

Introducing Apache Mahout
http://www.ibm.com/developerworks/java/library/j-mahout/
Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.

Apache Mahout: Scalable machine learning for everyone
http://www.ibm.com/developerworks/java/library/j-mahout-scaling/
Apache Mahout committer Grant Ingersoll brings you up to speed on the current version of the Mahout machine-learning library and walks through an example of how to deploy and scale some of Mahout’s more popular algorithms.

Weka
http://www.cs.waikato.ac.nz/ml/weka/
http://en.wikipedia.org/wiki/Weka_(machine_learning)
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

(Python)

Scikit-learn
http://scikit-learn.org/
http://en.wikipedia.org/wiki/Scikit-learn
Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering.

Orange
http://orange.biolab.si/
http://en.wikipedia.org/wiki/Orange_(software)

(C++)

GraphLab
http://graphlab.org/
GraphLab is a graph-based, high performance, distributed computation framework written in C++. While GraphLab was originally developed for Machine Learning tasks, it has found great success at a broad range of other data-mining tasks; out-performing other abstractions by orders of magnitude.

mlpack
http://www.mlpack.org/
mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.

Waffles
http://waffles.sourceforge.net/
http://en.wikipedia.org/wiki/Waffles_(machine_learning)

(API)

Google Prediction API
https://developers.google.com/prediction/
Google’s cloud-based machine learning tools can help analyze your data to add the following features to your applications: Customer sentiment analysis, Spam detection, Message routing decisions, Upsell opportunity analysis, Document and email classification, Diagnostics, Churn analysis, Suspicious activity identification, Recommendation systems