Machine Learning

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=== Next Meeting===
=== Next Meeting===
*When: Some time in August?
*When: November 8 @ 7:30pm
*Where: 2169 Mission St. (back NE corner, Church classroom)
*Where: 2169 Mission St. (back NE corner, Church classroom)
*Topic: Basic Information Theory: Entropy
*Details: We're having a summer vacation of sorts, but feel free to self-organize and have a meetup by emailing the [ mailing list]!
*Details: Whiteboard discussion on the very basics of discrete probability distributions and entropy.
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*[ PyTables]
*[ PyTables]
**Adds querying capabilities to HDF5 files
**Adds querying capabilities to HDF5 files
*[ statsmodels]
**Regression, time series analysis, statistics stuff for python
*[ Vowpal Wabbit]
**"Intrinsically Fast" implementation of gradient descent for large datasets
*[ Shogun]
**Fast implementations of SVMs
*[ Stan]
**A graphical model compiler
==== Computer Vision ====
==== Computer Vision ====
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=== [[Machine Learning/Meeting Notes|Meeting Notes]]===
=== [[Machine Learning/Meeting Notes|Meeting Notes]]===

Revision as of 18:07, 15 November 2012


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://
  • 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


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


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