Difference between revisions of "Machine Learning"

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*Topic: Kaggle and Machine Learning, The Machine Learning of the Kinect
 
*Topic: Kaggle and Machine Learning, The Machine Learning of the Kinect
 
*Details: Anthony Goldbloom from Kaggle is going to come in and talk about things! Then if we have time we'll go over the kinect paper.
 
*Details: Anthony Goldbloom from Kaggle is going to come in and talk about things! Then if we have time we'll go over the kinect paper.
*Presenter: Anthony Goldbloom, Mike S
+
*Presenter: Anthony Goldbloom
  
 
=== Future Talks and Topics ===
 
=== Future Talks and Topics ===
 +
* The Kinect and Random Decision Forests (Mike S, 4/20/2011)
 
* Graphical Models, Tony
 
* Graphical Models, Tony
* Boltzmann Machines (Mike S, April 2011)
+
* Boltzmann Machines (Mike S, May 2011)
 
* Boosting and Bagging (Thomas, unscheduled)
 
* Boosting and Bagging (Thomas, unscheduled)
 
* [[CS229]] second problem set
 
* [[CS229]] second problem set

Revision as of 00:40, 13 April 2011

Next Meeting

  • When: Wednesday, 4/13/2011 @ 7:30-9:00pm
  • Where: 2169 Mission St. (back corner classroom)
  • Topic: Kaggle and Machine Learning, The Machine Learning of the Kinect
  • Details: Anthony Goldbloom from Kaggle is going to come in and talk about things! Then if we have time we'll go over the kinect paper.
  • Presenter: Anthony Goldbloom

Future Talks and Topics

  • The Kinect and Random Decision Forests (Mike S, 4/20/2011)
  • Graphical Models, Tony
  • Boltzmann Machines (Mike S, May 2011)
  • Boosting and Bagging (Thomas, unscheduled)
  • CS229 second problem set
  • RPy?

Mailing List

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

Projects

Datasets

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