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=== Join the Mailing List === | |||
https://www.noisebridge.net/mailman/listinfo/ml | |||
=== Next Meeting=== | === Next Meeting=== | ||
*When: | *When: Thursday, January 30, 2013 @ 7:00pm | ||
*Where: 2169 Mission St. ( | *Where: 2169 Mission St. (Church classroom) | ||
*Topic: | *Topic: k-Nearest Neighbors and k-Means Clustering | ||
*Details: | *Details: | ||
*Who: | *Who: Mike S | ||
=== Take the Noisebridge ML Survey === | === Take the Noisebridge ML Survey === | ||
[http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn! | [http://www.surveymonkey.com/s/W2T9ZB6 Take a survey] and vote for what you want to learn! | ||
=== Talks and Workshops === | === Talks and Workshops === | ||
Line 42: | Line 40: | ||
*Working with the Kinect | *Working with the Kinect | ||
*Computer Vision with OpenCV | *Computer Vision with OpenCV | ||
=== Projects === | === Projects === | ||
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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!] | ||
*[http://www.sipri.org/databases SIPRI Social Databases] | |||
**Wealth of information on international arms transfers and peace missions. | |||
*[http://aws.amazon.com/publicdatasets/ Amazon AWS Public Datasets] | |||
*[http://www.prio.no/Data/Armed-Conflict/ UCDP/PRIO Armed Conflict Datasets] | |||
*[https://opendata.socrata.com/browse Socrata Government Datasets] | |||
=== Software Tools === | === Software Tools === | ||
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*[http://www.cs.waikato.ac.nz/ml/weka/ Weka] | *[http://www.cs.waikato.ac.nz/ml/weka/ Weka] | ||
**a collection of data mining tools and machine learning algorithms. | **a collection of data mining tools and machine learning algorithms. | ||
*[http://scikit-learn.sourceforge.net/ scikits.learn] | *[http://scikit-learn.sourceforge.net/ scikits.learn] | ||
**Machine learning Python package | **Machine learning Python package | ||
Line 106: | Line 103: | ||
*[http://www.shogun-toolbox.org/ Shogun] | *[http://www.shogun-toolbox.org/ Shogun] | ||
**Fast implementations of SVMs | **Fast implementations of SVMs | ||
*[http://www.mlpack.org/ MLPACK] | |||
**High performance scalable ML Library | |||
*[http://www.torch.ch/ Torch] | |||
**MATLAB-like environment for state-of-the art ML libraries written in LUA | |||
==== Online ML ==== | |||
*[http://moa.cs.waikato.ac.nz/ MOA (Massive Online Analysis)] | |||
**Offshoot of weka, has all online-algorithms | |||
*[http://jubat.us/en/ Jubatus] | |||
**Distributed Online ML | |||
*[http://dogma.sourceforge.net/ DOGMA] | |||
**MATLAB-based online learning stuff | |||
*[http://code.google.com/p/libol/ libol] | |||
*[http://code.google.com/p/oll/ oll] | |||
*[http://code.google.com/p/scw-learning/ scw-learning] | |||
==== Graphical Models ==== | |||
*[http://www.mrc-bsu.cam.ac.uk/bugs/ BUGS] | |||
**MCMC for Bayesian Models | |||
*[http://mcmc-jags.sourceforge.net/ JAGS] | |||
**Hierarchical Bayesian Models | |||
*[http://mc-stan.org/ Stan] | *[http://mc-stan.org/ Stan] | ||
**A graphical model compiler | **A graphical model compiler | ||
*[https://github.com/kutschkem/Jayes Jayes] | |||
**Bayesian networks in Java | |||
*[http://tops.sourceforge.net/ ToPS] | |||
**Probabilistic models of sequences | |||
==== Text Stuff ==== | |||
*[http://www.crummy.com/software/BeautifulSoup/ Beautiful Soup] | |||
**Screen-scraping tools | |||
*[http://www.mlsec.org/sally/ SALLY] | |||
**Tool for embedding strings into vector spaces | |||
*[http://radimrehurek.com/gensim/ Gensim] | |||
**Topic modeling | |||
==== Collaborative Filtering ==== | |||
*[http://prea.gatech.edu/ PREA] | |||
**Personalized Recommendation Algorithms Toolkit | |||
*[http://svdfeature.apexlab.org/wiki/Main_Page SVDFeature] | |||
**Collaborative Filtering and Ranking Toolkit | |||
==== Computer Vision ==== | ==== Computer Vision ==== | ||
Line 114: | Line 150: | ||
**Has ML component (SVM, trees, etc) | **Has ML component (SVM, trees, etc) | ||
**Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here] | **Online tutorials [http://www.pages.drexel.edu/~nk752/tutorials.html here] | ||
*[http://drwn.anu.edu.au/ DARWIN] | |||
**Generic C++ ML and Computer Vision Library | |||
*[http://sourceforge.net/projects/petavision/ PetaVision] | |||
**Developing a real-time, full-scale model of the primate visual cortex. | |||
==== Audio Processing ==== | ==== Audio Processing ==== | ||
Line 122: | Line 162: | ||
*[https://github.com/jsawruk/pymir PYMir] | *[https://github.com/jsawruk/pymir PYMir] | ||
**A library for reading mp3's into python, and doing analysis | **A library for reading mp3's into python, and doing analysis | ||
*[http://www.fon.hum.uva.nl/praat/ PRAAT] | |||
**Speech analysis toolkit | |||
*[http://ofer.sci.ccny.cuny.edu/sound_analysis_pro Sound Analysis Pro] | |||
**Tool for analyzing animal sounds | |||
*[http://luscinia.sourceforge.net/ Luscinia] | |||
**Software for archiving, measuring, and analyzing bioacoustic data | |||
*[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python] | *[http://wiki.python.org/moin/PythonInMusic List of Sound Tools for Python] | ||
Line 133: | Line 180: | ||
*[http://code.enthought.com/projects/mayavi/ MayaVi2] | *[http://code.enthought.com/projects/mayavi/ MayaVi2] | ||
**3D Scientific Data Visualization | **3D Scientific Data Visualization | ||
*[http://cytoscape.github.io/cytoscape.js/ Cytoscape] | |||
**A JavaScript graph library for analysis and visualisation | |||
*[https://plot.ly/ plot.ly] | |||
**Web-based plotting | |||
==== Cluster Computing ==== | ==== Cluster Computing ==== | ||
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*[http://web.mit.edu/star/cluster/ STAR: Cluster] | *[http://web.mit.edu/star/cluster/ STAR: Cluster] | ||
**Easily build your own Python computing cluster on Amazon EC2 | **Easily build your own Python computing cluster on Amazon EC2 | ||
==== Database Stuff ==== | |||
*[http://madlib.net/ MADlib] | |||
**Machine learning algorithms for in-database data | |||
*[http://www.joyent.com/products/manta Manta] | |||
**Distributed object storage | |||
==== Neural Simulation ==== | |||
*[http://nengo.ca/ Nengo] | |||
==== Other ==== | ==== Other ==== |
Revision as of 21:42, 23 January 2014
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!
- Intro to Machine Learning
- A Brief Tour of Statistics
- Generalized Linear Models
- Neural Nets Workshop
- Support Vector Machines
- Random Forests
- Independent Components Analysis
- Deep Nets
Code and SourceForge Site
- We have a Sourceforge Project
- We have a git repository on the project page, accessible as:
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
- Small Group Subproblems
- Fundraising
- Noisebridge Machine Learning Course
- Kaggle Social Network Contest
- KDD Competition 2010
- HIV
Datasets and Websites
- UCI Machine Learning Repository
- DataSF.org
- Infochimps
- Face Recognition Databases
- Time Series Data Library
- Data Q&A Forum
- Metaoptimize
- Quora ML Page
- A ton of Weather Data
- MLcomp
- Upload your algorithm and objectively compare it's performance to other algorithms
- Social Security Death Master File!
- SIPRI Social Databases
- Wealth of information on international arms transfers and peace missions.
- Amazon AWS Public Datasets
- UCDP/PRIO Armed Conflict Datasets
- Socrata Government Datasets
Software Tools
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.
- LIBSVM
- c-based SVM package
- PyML
- MDP
- Modular framework, has lots of stuff!
- VirtualBox Virtual Box Image with Pre-installed Libraries listed here
- Theano: Symbolic Expressions and Transparent GPU Integration
- 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
- MLPACK
- High performance scalable ML Library
- Torch
- MATLAB-like environment for state-of-the art ML libraries written in LUA
Online ML
- MOA (Massive Online Analysis)
- Offshoot of weka, has all online-algorithms
- Jubatus
- Distributed Online ML
- DOGMA
- MATLAB-based online learning stuff
- libol
- oll
- scw-learning
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
- Beautiful Soup
- Screen-scraping tools
- SALLY
- Tool for embedding strings into vector spaces
- Gensim
- Topic modeling
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
Neural Simulation
Other
Presentations and other Materials
- Awesome Machine Learning Applications -- A list of cool applications of ML
- Hands-on Machine Learning, a presentation jbm gave on 2009-01-07.
- http://www.youtube.com/user/StanfordUniversity#g/c/A89DCFA6ADACE599 Stanford Machine Learning online course videos]
- Media:Brief_statistics_slides.pdf, a presentation given on statistics for the machine learning group
- LinkedIn discussion on good resources for data mining and predictive analytics
- Face Recognition Algorithms
- Max Welling's ML classnotes
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
- Unsupervised Learning
- Hidden Markov Models
- Clustering: PCA, k-Means, Expectation-Maximization
- Graphical Modeling
- Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes
- Deep Belief Networks & Restricted Boltzmann Machines
- 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
- Applications
- Collective Intelligence & Recommendation Engines