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=== Join the Mailing List === | |||
https://www.noisebridge.net/mailman/listinfo/ml | |||
=== Next Meeting=== | === Next Meeting=== | ||
*When: | *When: Thursday, February 13, 2014 @ 6:30pm | ||
*Where: 2169 Mission St. | *Where: 2169 Mission St. (Church classroom) | ||
*Topic: Linear | *Topic: Bayesian Inference for everyone | ||
* | *Details: | ||
* | *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/Brief Tour of Statistics|A Brief Tour of Statistics]] | |||
*[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]] | |||
*[[NBML/Workshops/Neural Nets|Neural Nets Workshop]] | |||
*[[NBML/Workshops/Support Vector Machines|Support Vector Machines]] | |||
*[[NBML/Workshops/Random Forests|Random Forests]] | |||
*[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]] | |||
*[[NBML/Workshops/Deep Nets|Deep Nets]] | |||
=== Code and SourceForge Site === | |||
*We have a [http://sourceforge.net/projects/ml-noisebridge 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]] | |||
*[[Machine Learning/Fundraising | Fundraising]] | |||
*[[NBML_Course|Noisebridge Machine Learning Course]] | |||
*[[Machine Learning/Kaggle Social Network Contest | Kaggle Social Network Contest]] | |||
*[[KDD Competition 2010]] | |||
*[[Machine Learning/Kaggle HIV | HIV]] | |||
=== [[Machine_Learning/Datasets|Datasets and Websites]] === | |||
*[http://archive.ics.uci.edu/ml/ UCI Machine Learning Repository] | |||
*[[DataSF.org]] | |||
*[http://infochimps.com/ Infochimps] | |||
*[http://www.face-rec.org/databases/ Face Recognition Databases] | |||
*[http://robjhyndman.com/TSDL/ Time Series Data Library] | |||
*[http://getthedata.org/ Data Q&A Forum] | |||
*[http://metaoptimize.com/qa/ Metaoptimize] | |||
*[http://www.quora.com/Machine-Learning Quora ML Page] | |||
*[http://www.metoffice.gov.uk/research/climate/climate-monitoring/land-and-atmosphere/surface-station-records A ton of Weather Data] | |||
*[http://mlcomp.org/ MLcomp] | |||
**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.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] | |||
*[http://us-city.census.okfn.org/ US City Census Data] | |||
*[http://webscope.sandbox.yahoo.com/catalog.php Yahoo Labs Datasets] | |||
=== Software Tools === | |||
==== Generic ML Libraries ==== | |||
*[http://www.cs.waikato.ac.nz/ml/weka/ Weka] | |||
**a collection of data mining tools and machine learning algorithms. | |||
*[http://scikit-learn.sourceforge.net/ scikits.learn] | |||
**Machine learning Python package | |||
*[http://pypi.python.org/pypi/scikits.statsmodels scikits.statsmodels] | |||
**Statistical models to go with scipy | |||
*[http://pybrain.org PyBrain] | |||
**Does feedforward, recurrent, SOM, deep belief nets. | |||
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ LIBSVM] | |||
**c-based SVM package | |||
*[http://pyml.sourceforge.net PyML] | |||
*[http://mdp-toolkit.sourceforge.net/ MDP] | |||
**Modular framework, has lots of stuff! | |||
*[[Machine Learning/VirtualBox|VirtualBox]] Virtual Box Image with Pre-installed Libraries listed here | |||
*[http://sympy.org sympy] Does symbolic math | |||
*[http://waffles.sourceforge.net/ Waffles] | |||
**Open source C++ set of machine learning command line tools. | |||
*[http://rapid-i.com/content/view/181/196/ RapidMiner] | |||
*[http://www.mrpt.org/ Mobile Robotic Programming Toolkit] | |||
*[http://nipy.sourceforge.net/nitime/ nitime] | |||
**NeuroImaging in Python, has some good time series analysis stuff and multi-variate response fitting. | |||
*[http://pandas.pydata.org/ Pandas] | |||
**Data analysis workflow in python | |||
*[http://www.pytables.org/moin PyTables] | |||
**Adds querying capabilities to HDF5 files | |||
*[http://statsmodels.sourceforge.net/ statsmodels] | |||
**Regression, time series analysis, statistics stuff for python | |||
*[https://github.com/JohnLangford/vowpal_wabbit/wiki Vowpal Wabbit] | |||
**"Intrinsically Fast" implementation of gradient descent for large datasets | |||
*[http://www.shogun-toolbox.org/ Shogun] | |||
**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 | |||
==== 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 ==== | |||
*[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] | |||
**A graphical model compiler | |||
*[https://github.com/kutschkem/Jayes Jayes] | |||
**Bayesian networks in Java | |||
*[http://tops.sourceforge.net/ ToPS] | |||
**Probabilistic models of sequences | |||
*[http://pymc-devs.github.io/pymc/ PyMC] | |||
**Bayesian Models in Python | |||
==== 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 ==== | |||
*[http://opencv.willowgarage.com/documentation/index.html OpenCV] | |||
**Computer Vision Library | |||
**Has ML component (SVM, trees, etc) | |||
**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 ==== | |||
*[http://tlecomte.github.com/friture/ Friture] | |||
**Real-time spectrogram generation | |||
*[http://code.google.com/p/pyo/ pyo] | |||
**Real-time audio signal processing | |||
*[https://github.com/jsawruk/pymir PYMir] | |||
**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://jasperproject.github.io/ Jasper] | |||
**Voice-control anything! | |||
==== Data Visualization ==== | |||
*[http://www.ailab.si/orange/ Orange] | |||
**Strong data visualization component | |||
*[http://gephi.org/ Gephi] | |||
**Graph Visualization | |||
*[http://had.co.nz/ggplot2/ ggplot] | |||
**Nice plotting package for R | |||
*[http://code.enthought.com/projects/mayavi/ MayaVi2] | |||
**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 | |||
*[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 ==== | |||
*[http://lucene.apache.org/mahout/ Mahout] | |||
**Hadoop cluster based ML package. | |||
*[http://web.mit.edu/star/cluster/ STAR: Cluster] | |||
**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 ==== | |||
*[http://jmlr.csail.mit.edu/mloss/ Journal of Machine Learning Software List] | |||
=== Presentations and other Materials === | |||
* [[Awesome Machine Learning Applications]] -- A list of cool applications of ML | |||
* [[Hands-on Machine Learning]], a presentation [[User:jbm|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 | |||
* [http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=20096092&gid=77616&trk=EML_anet_qa_ttle-0Nt79xs2RVr6JBpnsJt7dBpSBA LinkedIn] discussion on good resources for data mining and predictive analytics | |||
* [http://www.face-rec.org/algorithms/ Face Recognition Algorithms] | |||
* [http://www.ics.uci.edu/~welling/classnotes/classnotes.html Max Welling's ML classnotes] | |||
=== Topics to Learn and Teach === | === Topics to Learn and Teach === | ||
[[NBML Course]] - Noisebridge Machine Learning Curriculum (work-in-progress) | |||
[[CS229]] - The Stanford Machine learning Course @ noisebridge | |||
*Supervised Learning | *Supervised Learning | ||
**Linear Regression | **Linear Regression | ||
**Linear Discriminants | **Linear Discriminants | ||
**Neural Nets/Radial Basis Functions | **Neural Nets/Radial Basis Functions | ||
Line 18: | Line 263: | ||
*Unsupervised Learning | *Unsupervised Learning | ||
** | **[[Machine_Learning/HMM|Hidden Markov Models]] | ||
**k-Means | **Clustering: PCA, k-Means, Expectation-Maximization | ||
**Graphical Modeling | **Graphical Modeling | ||
**Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes | **Generative Models: gaussian distribution, multinomial distributions, HMMs, Naive Bayes | ||
**[[Machine_Learning/Deep_Belief_Networks|Deep Belief Networks & Restricted Boltzmann Machines]] | |||
*Reinforcement Learning | *Reinforcement Learning | ||
Line 45: | Line 291: | ||
** Collective Intelligence & Recommendation Engines | ** Collective Intelligence & Recommendation Engines | ||
=== | === [[Machine Learning/Meeting Notes|Meeting Notes]]=== | ||
[[ | [[Category:Events]] | ||
[[Category:Projects]] |
Revision as of 10:49, 9 April 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
- US City Census Data
- Yahoo Labs 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
- List of Sound Tools for Python
- Jasper
- Voice-control anything!
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