Machine Learning: Difference between revisions

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=== Next Meeting===
=== Next Meeting===


*When: Wednesday, 4/28/2010 @ 8:00pm
*When: Wednesday, 5/5/2010 @ 8:00pm
*Where: 2169 Mission St.
*Where: 2169 Mission St. (back corner classroom)
*Topic: Linear Regression
*Topic: A Brief Tour of Statistics
*Presenter: Kai, Mike S
*Presenter: Thomas
*Description: An overview of Support Vector Machines (Potentially)


=== Topics to Learn and Teach ===
=== Topics to Learn and Teach ===
Line 57: Line 56:


=== Notes from Meetings ===
=== Notes from Meetings ===
[[Machine Learning Meetup Notes: 2010-04-28]] -- SVMs


[[Machine Learning Meetup Notes: 2010-04-21]] -- Linear Regression
[[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: 2010-04-14]] -- (re)Starting new Machine Learning Meetup!
(We've fallen off the notes bandwagon, sorry.)


[[Machine Learning Meetup Notes: 2009-04-01]] -- Finally moving on up: fully-connected backpropagation networks.
[[Machine Learning Meetup Notes: 2009-04-01]] -- Finally moving on up: fully-connected backpropagation networks.

Revision as of 21:29, 28 April 2010

Next Meeting

  • When: Wednesday, 5/5/2010 @ 8:00pm
  • Where: 2169 Mission St. (back corner classroom)
  • Topic: A Brief Tour of Statistics
  • Presenter: Thomas

Topics to Learn and Teach

  • Supervised Learning
    • Linear Regression (Mike S volunteered to teach)
    • Linear Discriminants
    • Neural Nets/Radial Basis Functions
    • Support Vector Machines
    • Classifier Combination [1]
    • A basic decision tree builder, recursive and using entropy metrics
  • Unsupervised Learning
    • Clustering/PCA
    • k-Means Clustering
    • Graphical Modeling
    • Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
  • 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

Possible Projects

Presentations and other Materials


Notes from Meetings

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