Machine Learning: Difference between revisions

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*When: Wednesday, 7/20/2011 @ 7:30-9:00pm
*When: Wednesday, 7/20/2011 @ 7:30-9:00pm
*Where: 2169 Mission St. (back corner classroom)
*Where: 2169 Mission St. (back corner classroom)
*Topic: Undefined
*Topic: Talking about AI, robotics, image classification
*Details: Send an email to the list if you're interested in talking about a specific topic.
*Details: Very informal meetup to talk about potential projects, stop by!
*Presenters:
*Presenters:



Revision as of 14:54, 18 July 2011

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.

Next Meeting

  • When: Wednesday, 7/20/2011 @ 7:30-9:00pm
  • Where: 2169 Mission St. (back corner classroom)
  • Topic: Talking about AI, robotics, image classification
  • Details: Very informal meetup to talk about potential projects, stop by!
  • Presenters:

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!

Future Talks and Topics, Ideas

  • Boltzmann Machines, Deep Nets (Mike S, August 2011)
  • Graphical Models (Tony)
  • Working with the Kinect
  • Computer Vision with OpenCV

Mailing List

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

Projects

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