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=== Next Meeting=== | === Next Meeting=== | ||
*When: Thursday, | *When: Thursday, February 13, 2014 @ 6:30pm | ||
*Where: 2169 Mission St. (Church classroom) | *Where: 2169 Mission St. (Church classroom) | ||
*Topic: | *Topic: Bayesian Inference for everyone | ||
*Details: | *Details: | ||
*Who: | *Who: Sam Tepper | ||
=== | === Learn about Data Science and Machine Learning === | ||
=== | ===== Classes ===== | ||
*[https://www.coursera.org/course/ml Coursera Machine Learning Course with Andrew Ng] | |||
*[https://www.coursera.org/course/compneuro Coursera Computational Neuroscience Course with Adrienne Fairhall] | |||
*[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT Machine Learning Class with Tommi Jaakkola] | |||
*[http://cs229.stanford.edu/materials.html Stanford CS229] | |||
*[http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml Carnegie Mellon Machine Learning Course with Tom Mitchell] | |||
*[http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Linear Algebra with Gilbert Strang] | |||
*[https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH Neural Networks Class with Hugo Larochelle] | |||
==== Books ==== | |||
*[http://statweb.stanford.edu/~tibs/ElemStatLearn/ Elements of Statistical Learning] | |||
*[https://www.google.com/search?client=ubuntu&channel=fs&q=pattern+recognition+and+machine+learning&ie=utf-8&oe=utf-8#channel=fs&q=pattern+recognition+and+machine+learning+pdf Pattern Recognition and Machine Learning] | |||
*[https://www.google.com/search?&channel=fs&q=+Information+Theory%2C+Inference%2C+and+Learning+Algorithms.&ie=utf-8&oe=utf-8#channel=fs&q=Information+Theory%2C+Inference%2C+and+Learning+Algorithms+pdf Information Theory, Inference, and Learning Algorithms] | |||
*[http://chimera.labs.oreilly.com/books/1230000000345 Interactive Data Visualization for the Web (D3)] | |||
*[http://cran.r-project.org/doc/manuals/R-intro.pdf Introduction to R] | |||
*[http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf Introduction to Probability (Grinstead and Snell)] | |||
*[http://www.cis.temple.edu/~latecki/Courses/CIS2033-Spring12/A_modern_intro_probability_statistics_Dekking05.pdf Modern Introduction to Probability and Statistics (Kraaikamp and Meester)] | |||
*[http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf Bayesian Reasoning and Machine Learning] | |||
==== Tutorials ==== | |||
*[http://nbviewer.ipython.org/github/unpingco/Python-for-Signal-Processing/tree/master/ Signal Processing IPython Notebooks] | |||
*[http://scikit-learn.org/stable/tutorial/basic/tutorial.html Introduction to ML with scikits.learn] | |||
*[http://www.sagemath.org/doc/tutorial/ Learn how to use SAGE] | |||
==== Noisebridge ML Class Slides ==== | |||
*[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]] | *[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]] | ||
*[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]] | *[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]] | ||
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**Modular framework, has lots of stuff! | **Modular framework, has lots of stuff! | ||
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here | *[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here | ||
*[http://sympy.org sympy] Does symbolic math | *[http://sympy.org sympy] Does symbolic math | ||
*[http://waffles.sourceforge.net/ Waffles] | *[http://waffles.sourceforge.net/ Waffles] | ||
Line 107: | Line 128: | ||
*[http://www.torch.ch/ Torch] | *[http://www.torch.ch/ Torch] | ||
**MATLAB-like environment for state-of-the art ML libraries written in LUA | **MATLAB-like environment for state-of-the art ML libraries written in LUA | ||
==== Deep Nets ==== | |||
*[http://deeplearning.net/software/theano/ Theano] | |||
**Symbolic Expressions and Transparent GPU Integration | |||
*[http://caffe.berkeleyvision.org/ Caffe] | |||
**Convolutional Neural Networks on GPU | |||
*[https://code.google.com/p/neurolab/ Neurolab] | |||
**Has support for recurrent neural nets | |||
==== Online ML ==== | ==== Online ML ==== | ||
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*[http://tops.sourceforge.net/ ToPS] | *[http://tops.sourceforge.net/ ToPS] | ||
**Probabilistic models of sequences | **Probabilistic models of sequences | ||
*[http://pymc-devs.github.io/pymc/ PyMC] | |||
**Bayesian Models in Python | |||
==== Text Stuff ==== | ==== Text Stuff ==== | ||
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*[https://plot.ly/ plot.ly] | *[https://plot.ly/ plot.ly] | ||
**Web-based plotting | **Web-based plotting | ||
*[http://chimera.labs.oreilly.com/books/1230000000345/ch02.html D3 Ebook] | |||
**Has a good list of HTML/CSS/Javascript data visualization tools. | |||
*[https://plot.ly/ plotly] | |||
**Python plotting tool | |||
==== Cluster Computing ==== | ==== Cluster Computing ==== | ||
*[http://lucene.apache.org/mahout/ Mahout] | *[http://lucene.apache.org/mahout/ Mahout] | ||
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[[Category:Events]] | [[Category:Events]] | ||
[[Category:Projects]] | [[Category:Projects]] | ||
Revision as of 16:57, 18 March 2014
Join the Mailing List
https://www.noisebridge.net/mailman/listinfo/ml
Next Meeting
- When: Thursday, February 13, 2014 @ 6:30pm
- Where: 2169 Mission St. (Church classroom)
- Topic: Bayesian Inference for everyone
- Details:
- Who: Sam Tepper
Learn about Data Science and Machine Learning
Classes
- Coursera Machine Learning Course with Andrew Ng
- Coursera Computational Neuroscience Course with Adrienne Fairhall
- MIT Machine Learning Class with Tommi Jaakkola
- Stanford CS229
- Carnegie Mellon Machine Learning Course with Tom Mitchell
- Linear Algebra with Gilbert Strang
- Neural Networks Class with Hugo Larochelle
Books
- Elements of Statistical Learning
- Pattern Recognition and Machine Learning
- Information Theory, Inference, and Learning Algorithms
- Interactive Data Visualization for the Web (D3)
- Introduction to R
- Introduction to Probability (Grinstead and Snell)
- Modern Introduction to Probability and Statistics (Kraaikamp and Meester)
- Bayesian Reasoning and Machine Learning
Tutorials
Noisebridge ML Class Slides
- 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
- 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
Deep Nets
- Theano
- Symbolic Expressions and Transparent GPU Integration
- Caffe
- Convolutional Neural Networks on GPU
- Neurolab
- Has support for recurrent neural nets
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
- PyMC
- Bayesian Models in Python
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
- D3 Ebook
- Has a good list of HTML/CSS/Javascript data visualization tools.
- plotly
- Python plotting tool
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