Machine Learning

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=== Next Meeting===
=== Next Meeting===
Note: there is currently a study group for the [ Stanford ML Course] that meets up every Wednesday @ 7:30pm in the Church room, Monday meetups will be reserved for special presentations and announced here and on the [ mailing list].
*When: Thursday, 1/19/2012 @ 6:30-8:00pm
*Where: 2169 Mission St. (back corner, Church or Turing classroom, undecided)
*When: Wednesday, 11/16/2011 @ 7:30-9:00pm
*Topic: Sociological Datasets
*Where: 2169 Mission St. (back corner, Church classroom)
*Details: In preparation for 2012 ML workshops, we're going to evaluate some government and census datasets and brainstorm cool stuff to do with them.*Who: Mike S
*Topic: Working through [ review] for [ Stanford's ML Class]
*When: Monday, 11/28/2011 @ 7:30-9:00pm
*Where: 2169 Mission St. (back corner, Church classroom)
*Topic: Probability Distributions in Python, Gibb's Sampling for Restricted Boltzmann Machines
*Details: A gentle introduction to sampling from probability distributions using SciPy, and loose talk about how to do sampling for RBMs and Ising models
*Who: Mike S

Revision as of 18:52, 16 January 2012


Next Meeting

  • When: Thursday, 1/19/2012 @ 6:30-8:00pm
  • Where: 2169 Mission St. (back corner, Church or Turing classroom, undecided)
  • Topic: Sociological Datasets
  • Details: In preparation for 2012 ML workshops, we're going to evaluate some government and census datasets and brainstorm cool stuff to do with them.*Who: Mike S

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 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 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

  • Restricted Boltzmann Machines (Mike S, late August)
  • Deep Nets w/ Stacked Autoencoders (Mike S, September)
  • Generalized Linear Models (Mike S, September/October)
  • Graphical Models (Tony)
  • Working with the Kinect
  • Computer Vision with OpenCV

Mailing List


Datasets and Websites

Software Tools

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