Machine Learning: Difference between revisions

From Noisebridge
Jump to navigation Jump to search
Line 84: Line 84:


=== [[Machine Learning/Meeting Notes|Meeting Notes]]===
=== [[Machine Learning/Meeting Notes|Meeting Notes]]===
(Although we have stopped taking meeting notes, we meet up regularly...)
[[Machine Learning Meetup Notes:2010-11-17]]  -- Condensed feedback from Kaggle boards
[[Machine Learning Meetup Notes: 2010-11-03]] -- GLMS in R
[[Machine Learning Meetup Notes: 2010-10-27]] -- Linear Classification with scikits.learn
[[Machine Learning Meetup Notes: 2010-09-15]] -- Information Retrieval talk
[[Machine Learning Meetup Notes: 2010-08-25]] -- Organizing to go through CS 229
[[Machine Learning Meetup Notes: 2010-08-18]] -- Hidden Markov Models (HMMs)
[[Machine Learning Meetup Notes: 2010-07-21]] -- Intro to R
[[Machine Learning Meetup Notes: 2010-07-14]] -- Neural Networks
[[Machine Learning Meetup Notes: 2010-07-07]] -- Kaggle HIV, Edit Distance
[[Machine Learning Meetup Notes: 2010-06-30]] -- DNA Overview, Kaggle HIV
[[Machine Learning Meetup Notes: 2010-06-22]] -- PIG Tutorial
[[Machine Learning Meetup Notes: 2010-06-16]] -- MOA, Kaggle HIV
[[Machine Learning Meetup Notes: 2010-06-09]] -- KDD Recap, JUNG/Graph Clustering
[[Machine Learning Meetup Notes: 2010-06-02]] -- Final official meeting before KDD submission deadline
[[Machine Learning Meetup Notes: 2010-05-26]] -- Clustering, KDD Data Reduction
[[Machine Learning Meetup Notes: 2010-05-23]] -- Unofficial meetup to nail down KDD cup problem set
[[Machine Learning Meetup Notes: 2010-05-19]] -- Presentation on Hadoop and MapReduce
[[Machine Learning Meetup Notes: 2010-05-12]] -- Group workshop on KDD data set
[[Machine Learning Meetup Notes: 2010-05-05]] -- A Brief Tour of Statistics
[[Machine Learning Meetup Notes: 2010-04-28]] -- SVMs
[[Machine Learning Meetup Notes: 2010-04-21]] -- Linear Regression
[[Machine Learning Meetup Notes: 2010-04-14]] -- (re)Starting new Machine Learning Meetup!
[[Machine Learning Meetup Notes: 2009-04-01]] -- Finally moving on up: fully-connected backpropagation networks.
[[Machine Learning Meetup Notes: 2009-03-25]] -- We made perceptrons - added sigmoid, etc.
[[Machine Learning Meetup Notes: 2009-03-18]] -- We made perceptrons - linear function support!
[[Machine Learning Meetup Notes: 2009-03-11]] -- We made perceptrons!
Machine Learning Meetup Notes: 2009-03-04 -- Josh gave a presentation on SVMs
(time is missing!)
Machine Learning Meetup Notes: 2009-02-11 -- Josh gave a presentation on clustering, donuts!
Machine Learning Meetup Notes: 2009-02-04 -- Free-form hang out night, punch and pie
Machine Learning Meetup Notes: 2009-01-28 -- Praveen talked about Neural networks
Machine Learning Meetup Notes: 2008-01-21 -- Jean gave a quick overview of machine learning stuff
Machine Learning Meetup Notes: 2009-01-14 -- Ian gave a talk on k-Nearest Neighbor
Machine Learning Meetup Notes: 2009-01-07 -- Josh did a quick intro to ML presentation
[[Machine Learning Meetup Notes: 2008-12-17]]

Revision as of 01:00, 9 January 2011

Next Meeting

  • When: Wednesday, 1/12/2010 @ 7:30-9:00pm
  • Where: 2169 Mission St. (back corner classroom)
  • Topic: Semi-supervised Learning
  • Details:
  • Presenter: Clay W

Future Talks and Topics

  • Neural Network Workshop (Mike S, 1/26/2011)
  • Recurrent Neural Networks, Boltzmann Machines (Mike S, February 2011)
  • Boosting and Bagging (Thomas, unscheduled)
  • CS229 second problem set
  • RPy?

Mailing List

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

Projects

Datasets

Software Tools

Topics to Learn and Teach

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

Presentations and other Materials

Meeting Notes

Retrieved from "https://wiki.extremist.software/index.php?title=Machine_Learning&oldid=15740"