Pages tagged

PyBrain

PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.Gaussian Processes for Machine Learning: Contents

http://www.gaussianprocess.org/gpml/chapters/

Carl Edward Rasmussen and Christopher K. I. Williams MIT Press, 2006. ISBN-10 0-262-18253-X, ISBN-13 978-0-262-18253-9. This book is © Copyright 2006 by Massachusetts Institute of Technology. The MIT Press have kindly agreed to allow us to make the book available on the web. The web version of the book corresponds to the 2nd printing. You can buy the book for a list price of 36.00 US$ or 23.95 UK£. The whole book as a single pdf file.

Gaussian Processes for Machine LearningJDMP » Java Data Mining Package » About

http://www.jdmp.org/

The Java Data Mining Package (JDMP) is an open source Java library for data analysis and machine learning. It facilitates the access to data sources and machine learning algorithms (e.g. clustering, regression, classification, graphical models, optimization) and provides visualization modules. It includes a matrix library for storing and processing any kind of data, with the ability to handle very large matrices even when they do not fit into memory. Import and export interfaces are provided for JDBC data bases, TXT, CSV, Excel, Matlab, Latex, MTX, HTML, WAV, BMP and other file formats. JDMP provides a number of algorithms and tools, but also interfaces to other machine learning and data mining packages (Weka, LibSVM, Mallet, Lucene, Octave).

Data mining and visualisation tool that connects to a number of data sources (including Matlab and Weka)

datamining

LGPL 3Stanford School of Engineering

http://see.stanford.edu/SEE/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.Stephen Marsland

http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html

Stephen Marsland, Massey University

"I've written a textbook ... there are lots of Python code examples in the book, and the code is available here."

Machine Learning: An Algorithmic Perspective

"I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page."Feature Column from the AMS

http://www.ams.org/featurecolumn/archive/svd.html

An intuitive explanation of the geometric meaning behind SVD.

Good explanation of the SVD

Geometric interpretation of SVD.Stephen Marsland

http://www-ist.massey.ac.nz/smarsland/MLBook.html

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence.

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here.

Machine Learning: An Algorithmic Perspective

Python codes from a textbook entitled "Machine Learning: An Algorithmic Perspective"

by Stephen Marsland

I've written a textbook entitled "Machine Learning: An Algorithmic Perspective". It will be published by CRC Press, part of the Taylor and Francis group, on 2nd April 2009. The book is aimed at computer science and engineering undergraduates studing machine learning and artificial intelligence. There are lots of Python code examples in the book, and the code is available here. Where special datasets are used they are provided with the code, and there are links to additional datasets at the bottom of the page.Machine learning classifier gallery

http://home.comcast.net/~tom.fawcett/public_html/ML-gallery/pages/index.html

Interesting comparative performance of various algorithms on different data

A highly informative visualization of the biases of different ML classifiers. Really useful, especially for talks to non-experts.Netflix prize tribute: Recommendation algorithm in Python | This Number Crunching Life

http://blog.smellthedata.com/2009/06/netflix-prize-tribute-recommendation.html

Quick implementation of the Netflix recommendation algorithm (probablistic matrix factorization) in Python.

probabalistic matrix factorisation

I test my code using synthetic data, where I first make up latent vectors for users and items, then I generate some training set ratings by multiplying some latent user vectors by latent item vectors then adding some noise. I then discard the latent vectors and just give the model the synthetic ratings.Neural Networks - A Systematic Introduction

http://page.mi.fu-berlin.de/rojas/neural/

Looks like a comprehensive volume covering the state of art in late 90s. That's also about the time I stopped following the domain. So I wonder if there have been any advances in terms of new models and topologies since then?Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Hastie, Tibshirani and Friedman (2008). Springer-Verlag. Full-text PDF is free.

free online book

@dataspora: "The Elements of Statistical Learning, the authoritative text on the subject, now free at authors' site http://bit.ly/2J8WNK (ht @johndcook)" (from http://twitter.com/dataspora/status/4847621837)Guide to Getting Started in Machine Learning | A Beautiful WWW

http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/

Welcome to Elefant — Elefant

http://elefant.developer.nicta.com.au/

Efficient Learning, Large-scale Inference, and Optimisation Toolkit

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License

Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning licensed under the Mozilla Public License (MPL).A New Theory of Awesomeness and Miracles, by James Bridle, concerning Charles Babbage, Heath Robinson, MENACE and MAGE

http://shorttermmemoryloss.com/menace/

Being NOTES and SLIDES on a talk given at PLAYFUL 09, concerning CHARLES BABBAGE, HEATH ROBINSON, MENACE and MAGE

'...slightly larger than the Crab Nebula. And that is pretty awesome.'Collaborative Filtering with Ensembles - igvita.com

http://www.igvita.com/2009/09/01/collaborative-filtering-with-ensembles/

um nova tecnica para recomendação: Aplicar tecnicas especificas e juntar os resultadosMeasuring Measures: Learning About Statistical Learning

http://measuringmeasures.blogspot.com/2010/01/learning-about-statistical-learning.html

Stanford School of Engineering - Stanford Engineering Everywhere

http://see.stanford.edu/see/lecturelist.aspx?coll=63480b48-8819-4efd-8412-263f1a472f5a

Artificial Intelligence | Natural Language Processing

Natural Language/Artificial Intelligence LecturesGoogle Prediction API - Google Code

http://code.google.com/apis/predict/

Prediction API biedt mogelijkheden om bijv recommendations te doen op basis v historische data: http://bit.ly/c7z06p

Google Prediction APICOS 493, Spring 2002: Schedule and Readings

http://www.cs.princeton.edu/courses/archive/spring02/cs493/schedule.html

Algorithms for Massive Data SetsMetaOptimize Q+A - machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization

http://metaoptimize.com/qa/

brain - javascript neural networks

http://harthur.github.com/brain/

A neural network API in JavasriptYouTube - Lecture 1 | Machine Learning (Stanford)

http://www.youtube.com/watch?v=UzxYlbK2c7E

YouTube - Lecture 1 | Machine Learning (Stanford)

http://www.youtube.com/watch?v=UzxYlbK2c7E

YouTube - Lecture 1 | Machine Learning (Stanford)

http://www.youtube.com/watch?v=UzxYlbK2c7E

YouTube - Lecture 1 | Machine Learning (Stanford)

http://www.youtube.com/watch?v=UzxYlbK2c7E