Machine Learning: Difference between revisions

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*[http://infochimps.com/ Infochimps]
*[http://infochimps.com/ Infochimps]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://www.face-rec.org/databases/ Face Recognition Databases]
*[http://robjhyndman.com/TSDL/ Time Series Data Library]


=== [[Machine Learning/Tools | Software Tools]] ===
=== [[Machine Learning/Tools | Software Tools]] ===

Revision as of 23:22, 14 March 2011

Next Meeting

  • When: Wednesday, 3/9/2010 @ 7:30-9:00pm
  • Where: 2169 Mission St. (back corner classroom)
  • Topic: Introduction to Machine Learning
  • Details: What is machine learning? We'll go over some of the basics.
  • Presenter: Mike S

Future Talks and Topics

  • Graphical Models, Tony
  • Boltzmann Machines (Mike S, April 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