Machine Learning

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=== Next Meeting===
 
=== Next Meeting===
  
*When: September TBD @ 7:30pm
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*When:  
 
*Where: 2169 Mission St. (back NE corner, Church classroom)
 
*Where: 2169 Mission St. (back NE corner, Church classroom)
*Topic: Time series analysis Continuation
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*Topic:  
*Details: Fourier Transforms and Spectrograms
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*Details: Currently on hiatus until somebody decides to pick it back up!
*Who: Mike S
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*Who:  
  
 
=== Take the Noisebridge ML Survey ===
 
=== Take the Noisebridge ML Survey ===
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*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
 
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka]
 
**a collection of data mining tools and machine learning algorithms.
 
**a collection of data mining tools and machine learning algorithms.
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
 
**Offshoot of weka, has all online-algorithms
 
 
*[http://scikit-learn.sourceforge.net/ scikits.learn]
 
*[http://scikit-learn.sourceforge.net/ scikits.learn]
 
**Machine learning Python package
 
**Machine learning Python package
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*[http://www.shogun-toolbox.org/ Shogun]
 
*[http://www.shogun-toolbox.org/ Shogun]
 
**Fast implementations of SVMs
 
**Fast implementations of SVMs
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*[http://www.mlpack.org/ MLPACK]
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**High performance scalable ML Library
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*[http://www.torch.ch/ Torch]
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**MATLAB-like environment for state-of-the art ML libraries written in LUA
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==== Online ML ====
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*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)]
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**Offshoot of weka, has all online-algorithms
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*[http://jubat.us/en/ Jubatus]
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**Distributed Online ML
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*[http://dogma.sourceforge.net/ DOGMA]
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**MATLAB-based online learning stuff
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*[http://code.google.com/p/libol/ libol]
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*[http://code.google.com/p/oll/ oll]
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*[http://code.google.com/p/scw-learning/ scw-learning]
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==== Graphical Models ====
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*[http://www.mrc-bsu.cam.ac.uk/bugs/ BUGS]
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**MCMC for Bayesian Models
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*[http://mcmc-jags.sourceforge.net/ JAGS]
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**Hierarchical Bayesian Models
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*[http://mc-stan.org/ Stan]
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**A graphical model compiler
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*[https://github.com/kutschkem/Jayes Jayes]
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**Bayesian networks in Java
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==== Text Stuff ====
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*[http://www.crummy.com/software/BeautifulSoup/ Beautiful Soup]
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**Screen-scraping tools
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*[http://www.mlsec.org/sally/ SALLY]
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**Tool for embedding strings into vector spaces
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==== Collaborative Filtering ====
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*[http://prea.gatech.edu/ PREA]
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**Personalized Recommendation Algorithms Toolkit
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*[http://svdfeature.apexlab.org/wiki/Main_Page SVDFeature]
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**Collaborative Filtering and Ranking Toolkit
  
 
==== Computer Vision ====
 
==== Computer Vision ====
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**Has ML component (SVM, trees, etc)
 
**Has ML component (SVM, trees, etc)
 
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
 
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here]
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*[http://drwn.anu.edu.au/ DARWIN]
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**Generic C++ ML and Computer Vision Library
  
 
==== Audio Processing ====
 
==== Audio Processing ====
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*[https://github.com/jsawruk/pymir PYMir]  
 
*[https://github.com/jsawruk/pymir PYMir]  
 
**A library for reading mp3's into python, and doing analysis  
 
**A library for reading mp3's into python, and doing analysis  
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*[http://www.fon.hum.uva.nl/praat/ PRAAT]
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**Speech analysis toolkit
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*[http://ofer.sci.ccny.cuny.edu/sound_analysis_pro Sound Analysis Pro]
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**Tool for analyzing animal sounds
 
*[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python]
 
*[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python]
  
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*[http://code.enthought.com/projects/mayavi/ MayaVi2]
 
*[http://code.enthought.com/projects/mayavi/ MayaVi2]
 
**3D Scientific Data Visualization
 
**3D Scientific Data Visualization
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*[http://cytoscape.github.io/cytoscape.js/ Cytoscape]
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**A JavaScript graph library for analysis and visualisation
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*[https://plot.ly/ plot.ly]
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**Web-based plotting
  
 
==== Cluster Computing ====
 
==== Cluster Computing ====
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*[http://web.mit.edu/star/cluster/ STAR: Cluster]
 
*[http://web.mit.edu/star/cluster/ STAR: Cluster]
 
**Easily build your own Python computing cluster on Amazon EC2
 
**Easily build your own Python computing cluster on Amazon EC2
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 +
==== Database Stuff ====
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*[http://madlib.net/ MADlib]
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**Machine learning algorithms for in-database data
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*[http://www.joyent.com/products/manta Manta]
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**Distributed object storage
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==== Neural Simulation ====
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*[http://nengo.ca/ Nengo]
  
 
==== Other ====
 
==== Other ====
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=== [[Machine Learning/Meeting Notes|Meeting Notes]]===
 
=== [[Machine Learning/Meeting Notes|Meeting Notes]]===
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[[Category:Events]]
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[[Category:Projects]]

Revision as of 14:49, 16 October 2013

Contents

Next Meeting

  • When:
  • Where: 2169 Mission St. (back NE corner, Church classroom)
  • Topic:
  • Details: Currently on hiatus until somebody decides to pick it back up!
  • Who:

Take the Noisebridge ML Survey

Take a survey and vote for what you want to learn!

Crowdsourced Q&A

Are you working on a data mining, machine learning, or statistics problem? Do you want some help? Consider sending an email to the mailing list about it! Also consider setting up a day to come in and talk about the project you're working on and get input from Spotsylvania reckless driving other ML people.

About Us

We're a loosely-knit stochastic federation of people who like Noisebridge and like machine learning. What is machine learning? It's broad field that typically involves training computer models to solve problems. How can you website personalization participate? Join the mailing list, send an email and introduce yourself. Show up to the next meeting, share your thoughts. Participate in projects or start your own. Go to workshops, write code at workshops, learn stuff, give workshops of your own! All are welcome.

Talks and Workshops

We've given lots of workshops and talks over the past year or so, here's a few. Many of the workshops we've given previously are recurring and will be given again, especially upon request!

Code and SourceForge Site

    git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
  • Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

Future Talks and Topics, Ideas

  • Random Forests in R
  • Restricted Boltzmann Machines (Mike S, some day)
  • Analyzing brain cells (Mike S)
  • Deep Nets w/ Stacked Autoencoders (Mike S, some day)
  • Generalized Linear Models (Mike S, Erin L? some day)
  • Graphical Models
  • Working with the Kinect
  • Computer Vision with OpenCV

Mailing List

https://www.noisebridge.net/mailman/listinfo/ml

Projects

Datasets and Websites

Software Tools

Generic ML Libraries

Online ML

Graphical Models

  • BUGS
    • MCMC for Bayesian Models
  • JAGS
    • Hierarchical Bayesian Models
  • Stan
    • A graphical model compiler
  • Jayes
    • Bayesian networks in Java

Text Stuff

Collaborative Filtering

  • PREA
    • Personalized Recommendation Algorithms Toolkit
  • SVDFeature
    • Collaborative Filtering and Ranking Toolkit

Computer Vision

  • OpenCV
    • Computer Vision Library
    • Has ML component (SVM, trees, etc)
    • Online tutorials here
  • DARWIN
    • Generic C++ ML and Computer Vision Library

Audio Processing

Data Visualization

  • Orange
    • Strong data visualization component
  • Gephi
    • Graph Visualization
  • ggplot
    • Nice plotting package for R
  • MayaVi2
    • 3D Scientific Data Visualization
  • Cytoscape
    • A JavaScript graph library for analysis and visualisation
  • plot.ly
    • Web-based plotting

Cluster Computing

  • Mahout
    • Hadoop cluster based ML package.
  • STAR: Cluster
    • Easily build your own Python computing cluster on Amazon EC2

Database Stuff

  • MADlib
    • Machine learning algorithms for in-database data
  • Manta
    • Distributed object storage

Neural Simulation

Other

Presentations and other Materials

Topics to Learn and Teach

NBML Course - Noisebridge Machine Learning Curriculum (work-in-progress)

CS229 - The Stanford Machine learning Course @ noisebridge

  • Supervised Learning
    • Linear Regression
    • Linear Discriminants
    • Neural Nets/Radial Basis Functions
    • Support Vector Machines
    • Classifier Combination [1]
    • A basic decision tree builder, recursive and using entropy metrics
  • Reinforcement Learning
    • Temporal Difference Learning
  • Math, Probability & Statistics
    • Metric spaces and what they mean
    • Fundamentals of probabilities
    • Decision Theory (Bayesian)
    • Maximum Likelihood
    • Bias/Variance Tradeoff, VC Dimension
    • Bagging, Bootstrap, Jacknife [2]
    • Information Theory: Entropy, Mutual Information, Gaussian Channels
    • Estimation of Misclassification [3]
    • No-Free Lunch Theorem [4]
  • Machine Learning SDK's
    • OpenCV ML component (SVM, trees, etc)
    • Mahout a Hadoop cluster based ML package.
    • Weka a collection of data mining tools and machine learning algorithms.
  • Applications
    • Collective Intelligence & Recommendation Engines

Meeting Notes

Personal tools