Machine Learning

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=== Next Meeting===
 
=== Next Meeting===
  
*When: Tuesday, November 19, 2013 @ 7:00pm
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*When: Thursday, January 30, 2013 @ 7:00pm
*Where: 2169 Mission St. (Turing or Church classroom)
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*Where: 2169 Mission St. (Church classroom)
*Topic:  
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*Topic: k-Nearest Neighbors and k-Means Clustering
*Details: An introductory meeting. Come on out!
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*Details:  
*Who:
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*Who: Mike S
  
 
=== Take the Noisebridge ML Survey ===
 
=== Take the Noisebridge ML Survey ===
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**Upload your algorithm and objectively compare it's performance to other algorithms
 
**Upload your algorithm and objectively compare it's performance to other algorithms
 
*[http://www.ntis.gov/products/ssa-dmf.aspx Social Security Death Master File!]
 
*[http://www.ntis.gov/products/ssa-dmf.aspx Social Security Death Master File!]
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*[http://www.sipri.org/databases SIPRI Social Databases]
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**Wealth of information on international arms transfers and peace missions.
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*[http://aws.amazon.com/publicdatasets/ Amazon AWS Public Datasets]
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*[http://www.prio.no/Data/Armed-Conflict/ UCDP/PRIO Armed Conflict Datasets]
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*[https://opendata.socrata.com/browse Socrata Government Datasets]
  
 
=== Software Tools ===
 
=== Software Tools ===
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*[https://github.com/kutschkem/Jayes Jayes]
 
*[https://github.com/kutschkem/Jayes Jayes]
 
**Bayesian networks in Java
 
**Bayesian networks in Java
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*[http://tops.sourceforge.net/ ToPS]
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**Probabilistic models of sequences
  
 
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Revision as of 21:42, 23 January 2014

Contents

Join the Mailing List

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

Next Meeting

  • When: Thursday, January 30, 2013 @ 7:00pm
  • Where: 2169 Mission St. (Church classroom)
  • Topic: k-Nearest Neighbors and k-Means Clustering
  • Details:
  • Who: Mike S

Take the Noisebridge ML Survey

Take a survey and vote for what you want to learn!

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!

Code and SourceForge Site

    git clone git://ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
  • Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

Future Talks and Topics, Ideas

  • Random Forests in R
  • Restricted Boltzmann Machines (Mike S, some day)
  • Analyzing brain cells (Mike S)
  • Deep Nets w/ Stacked Autoencoders (Mike S, some day)
  • Generalized Linear Models (Mike S, Erin L? some day)
  • Graphical Models
  • Working with the Kinect
  • Computer Vision with OpenCV

Projects

Datasets and Websites

Software Tools

Generic ML Libraries

Online ML

Graphical Models

  • BUGS
    • MCMC for Bayesian Models
  • JAGS
    • Hierarchical Bayesian Models
  • Stan
    • A graphical model compiler
  • Jayes
    • Bayesian networks in Java
  • ToPS
    • Probabilistic models of sequences

Text Stuff

Collaborative Filtering

  • PREA
    • Personalized Recommendation Algorithms Toolkit
  • SVDFeature
    • Collaborative Filtering and Ranking Toolkit

Computer Vision

  • OpenCV
    • Computer Vision Library
    • Has ML component (SVM, trees, etc)
    • Online tutorials here
  • DARWIN
    • Generic C++ ML and Computer Vision Library
  • PetaVision
    • Developing a real-time, full-scale model of the primate visual cortex.

Audio Processing

  • Friture
    • Real-time spectrogram generation
  • pyo
    • Real-time audio signal processing
  • PYMir
    • A library for reading mp3's into python, and doing analysis
  • PRAAT
    • Speech analysis toolkit
  • Sound Analysis Pro
    • Tool for analyzing animal sounds
  • Luscinia
    • Software for archiving, measuring, and analyzing bioacoustic data

Data Visualization

  • Orange
    • Strong data visualization component
  • Gephi
    • Graph Visualization
  • ggplot
    • Nice plotting package for R
  • MayaVi2
    • 3D Scientific Data Visualization
  • Cytoscape
    • A JavaScript graph library for analysis and visualisation
  • plot.ly
    • Web-based plotting

Cluster Computing

  • Mahout
    • Hadoop cluster based ML package.
  • STAR: Cluster
    • Easily build your own Python computing cluster on Amazon EC2

Database Stuff

  • MADlib
    • Machine learning algorithms for in-database data
  • Manta
    • Distributed object storage

Neural Simulation

Other

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

Personal tools