Machine Learning

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=== Next Meeting===
 
=== Next Meeting===
  
*When: 3/8/2012
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*When: November 8 @ 7:30pm
*Where: 2169 Mission St. (back corner, Church or Turing classroom, undecided)
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*Where: 2169 Mission St. (back NE corner, Church classroom)
*Topic: Bring your dataset to Noisebridge!
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*Topic: Basic Information Theory: Entropy
*Details: Bring in a dataset you're interested in and share it, get opinions and ideas on how to work with it.
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*Details: Whiteboard discussion on the very basics of discrete probability distributions and entropy.
*Who: Everyone and anyone!
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*Who:  
  
 
=== Take the Noisebridge ML Survey ===
 
=== Take the Noisebridge ML Survey ===
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=== About Us ===
 
=== 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 participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml 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.
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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 <span class="plainlinks">[http://www.monoloop.com<span style="color:black;font-weight:normal; text-decoration:none!important; background:none!important; text-decoration:none;">website personalization</span>] participate? Join the [https://www.noisebridge.net/mailman/listinfo/ml 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 ===
 
=== Talks and Workshops ===
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=== Future Talks and Topics, Ideas ===
 
=== Future Talks and Topics, Ideas ===
*Random Forests in R (Mike S, 1/26)
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*Random Forests in R
 
*Restricted Boltzmann Machines (Mike S, some day)
 
*Restricted Boltzmann Machines (Mike S, some day)
 
*Analyzing brain cells (Mike S)
 
*Analyzing brain cells (Mike S)
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*[http://www.ntis.gov/products/ssa-dmf.aspx Social Security Death Master File!]
 
*[http://www.ntis.gov/products/ssa-dmf.aspx Social Security Death Master File!]
  
=== Software Tools]] ===
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=== Software Tools ===
  
 
==== Generic ML Libraries ====
 
==== Generic ML Libraries ====
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*[http://nipy.sourceforge.net/nitime/ nitime]
 
*[http://nipy.sourceforge.net/nitime/ nitime]
 
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.
 
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.
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*[http://pandas.pydata.org/ Pandas]
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**Data analysis workflow in python
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*[http://www.pytables.org/moin PyTables]
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**Adds querying capabilities to HDF5 files
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*[http://statsmodels.sourceforge.net/ statsmodels]
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**Regression, time series analysis, statistics stuff for python
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*[https://github.com/JohnLangford/vowpal_wabbit/wiki Vowpal Wabbit]
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**"Intrinsically Fast" implementation of gradient descent for large datasets
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*[http://www.shogun-toolbox.org/ Shogun]
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**Fast implementations of SVMs
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*[http://mc-stan.org/ Stan]
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**A graphical model compiler
  
 
==== Computer Vision ====
 
==== Computer Vision ====
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*[http://code.google.com/p/pyo/ pyo]
 
*[http://code.google.com/p/pyo/ pyo]
 
**Real-time audio signal processing
 
**Real-time audio signal processing
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*[https://github.com/jsawruk/pymir PYMir]
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**A library for reading mp3's into python, and doing analysis
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*[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python]
  
 
==== Data Visualization ====
 
==== Data Visualization ====
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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 18:07, 15 November 2012

Contents

Next Meeting

  • When: November 8 @ 7:30pm
  • Where: 2169 Mission St. (back NE corner, Church classroom)
  • Topic: Basic Information Theory: Entropy
  • Details: Whiteboard discussion on the very basics of discrete probability distributions and entropy.
  • 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

Computer Vision

  • OpenCV
    • Computer Vision Library
    • Has ML component (SVM, trees, etc)
    • Online tutorials here

Audio Processing

Data Visualization

  • Orange
    • Strong data visualization component
  • Gephi
    • Graph Visualization
  • ggplot
    • Nice plotting package for R
  • MayaVi2
    • 3D Scientific Data Visualization

Cluster Computing

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

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