Wednesday, February 11, 2015

Machine Learning

Machine Learning



[edit]Join the Mailing List

[edit]Next Meeting

  • When: Thursday, August 14, 2014 @ 6:00pm
  • Where: 2169 Mission St. (Church classroom)
  • Topic:
  • Details:
  • Who: Andy McMurry

[edit]Learn about Data Science and Machine Learning




[edit]Noisebridge ML Class Slides

[edit]Code and SourceForge Site

    git clone git://
  • Send an email to the list if you want to become an administrator on the site to get write access to the git repo!

[edit]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


[edit]Datasets and Websites

[edit]Software Tools

[edit]Generic ML Libraries

  • Weka
    • a collection of data mining tools and machine learning algorithms.
  • scikits.learn
    • Machine learning Python package
  • scikits.statsmodels
    • Statistical models to go with scipy
  • PyBrain
    • Does feedforward, recurrent, SOM, deep belief nets.
    • c-based SVM package
  • PyML
  • MDP
    • Modular framework, has lots of stuff!
  • VirtualBox Virtual Box Image with Pre-installed Libraries listed here
  • sympy Does symbolic math
  • Waffles
    • Open source C++ set of machine learning command line tools.
  • RapidMiner
  • Mobile Robotic Programming Toolkit
  • nitime
    • NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting.
  • Pandas
    • Data analysis workflow in python
  • PyTables
    • Adds querying capabilities to HDF5 files
  • statsmodels
    • Regression, time series analysis, statistics stuff for python
  • Vowpal Wabbit
    • "Intrinsically Fast" implementation of gradient descent for large datasets
  • Shogun
    • Fast implementations of SVMs
    • High performance scalable ML Library
  • Torch
    • MATLAB-like environment for state-of-the art ML libraries written in LUA

[edit]Deep Nets

  • Theano
    • Symbolic Expressions and Transparent GPU Integration
  • Caffe
    • Convolutional Neural Networks on GPU
  • Neurolab
    • Has support for recurrent neural nets

[edit]Online ML

[edit]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
  • PyMC
    • Bayesian Models in Python

[edit]Text Stuff

[edit]Collaborative Filtering

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

[edit]Computer Vision

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

[edit]Audio Processing

[edit]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
    • Web-based plotting
  • D3 Ebook
    • Has a good list of HTML/CSS/Javascript data visualization tools.
  • plotly
    • Python plotting tool

[edit]Cluster Computing

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

[edit]Database Stuff

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

[edit]Neural Simulation


[edit]Presentations and other Materials

[edit]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

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