In “Machine Learning Techniques for Stock Prediction”, Vatsal H. Shah (2007) evaluates several machine learning techniques applied to stock market prediction. The techniques used are: support vector machines, linear regression, “prediction using decision stumps”, expert weighting, text data mining, and online learning (the code was from YALE/Weka). The main stock features used were moving averages, exponential moving average, rate of change, and relative strength index. He concludes with “Of all the Algorithms we applied, we saw that only Support Vector Machine combined with Boosting gave us satisfactory results.

In “A Review of Studies on Machine Learning Techniques”, Singh, Bhatia, and Sangwan (2007) comment on neural nets, self organizing maps, case based reasoning, classification trees (CART), rule induction, and genetic algorithms. They include a nice chart at the end of the article that could be quite useful for managers.

In “Machine Learning Techniques—Reductions Between Prediction Quality Metrics” Beygelzimer, Langford, and Zadrozny (2009?) summarize a bunch of “techniques, called reductions, for converting a problem of minimizing one loss function into a problem of minimizing another, simpler loss function.” They give a simplified overview of machine learning algorithms and sampling methods relating them to error correcting codes and regret minimization.

Machine Learning Links from Google and http://www.cs.waikato.ac.nz/~bernhard/good-machine-learning-blogs.html
Long, Informative Articles
http://www.swkorridor.dk/en/blogs/machine_learning_applications/

Computer Vision, Image Processing Blog
http://quantombone.blogspot.com/

Causality Blog
http://www.mii.ucla.edu/causality/

Stack Exchange for Statistics
http://stats.stackexchange.com/

Machine Learning News Google Group
https://groups.google.com/forum/?fromgroups#!forum/ml-news

MetaOptimize Stack Exchange
http://metaoptimize.com/qa/

Reddit Machine Learning
http://www.reddit.com/r/machinelearning

Stack Overflow Datamining
http://stackoverflow.com/questions/tagged/data-mining

Stack Overflow Machine Learning
http://stackoverflow.com/questions/tagged/machine-learning

MLoss
http://mloss.org/community/
Software, Machine Learning, Science and Math

Julia language http://julialang.org/blog/

Alexandre Passos’ research blog
http://atpassos.posterous.com/
Real Commentary on Real Machine Learning Techniques & Papers

Anand Sarwate
http://ergodicity.net/
Frequent, Varied articles including ML

Peekaboo Andy’s Computer Vision and Machine Learning Blog
http://peekaboo-vision.blogspot.com/

Andrew Eckford: The Blog
http://andreweckford.blogspot.com/
Lots of notes about conferences

Andrew Rosenberg
http://spokenlanguageprocessing.blogspot.com/
Great Material on NLP and ML

Freakonomics
http://freakonometrics.blog.free.fr/index.php/
Read for fun

Brian Chesney
http://bpchesney.org/
Informative, Numer Analysis, Optimization, ML

Daniel Lemire’s blog
http://lemire.me/blog/
Interesting Thoughts on Science, Software, and Global Warming

Frank Nielsen: Computational Information Geometry Wonderland
http://blog.informationgeometry.org/index.php
Blog on Information Theory, Image Processing, Statistics, …

Igor Carron’s Nuit Blanche
http://nuit-blanche.blogspot.com/
Great blog on Statistics, Modelling Dynamic Systems, Data Mining, Compressive Sensing, Signal Processing, …

Jonathan Manton’s Blog
http://jmanton.wordpress.com/
A mathematician writes numerous in-depth posts on Numerical Analysis, Software, Probability, Teaching, …

Jürgen Schmidhuber’s Home Page
http://www.idsia.ch/~juergen/
Not a blog, but it is a good resource

Paul Mineiro: Machined Learnings
http://www.machinedlearnings.com/
Many posts

Radford Neal’s Blog
http://radfordneal.wordpress.com/
Theory of Statstics and Information Theory

Rob Hyndman: Research tips
http://robjhyndman.com/researchtips/
Forecasting

Rod Carvalho: Stochastix
http://stochastix.wordpress.com/
Math, Probability, Haskell, Numerical Methods,

Roman Shapovalov: Computer Blindness
http://computerblindness.blogspot.com/
Graphical Models, Learning Theory, Computer Vision, ML

Shubhendu Trivedi: Onionesque Reality
http://onionesquereality.wordpress.com/
Personal Blog, Abstract Ideas, Math, ML, …

Suresh: The Geomblog
http://geomblog.blogspot.com/
Teaching, DataMining, ML, Geometry, Computational Geometry

Roman Shapovalov: Computer Blindness
http://computerblindness.blogspot.com/
Graphical Models, Learning Theory, Computer Vision, ML

Sami Badawi: Hadoop comparison
http://blog.samibadawi.com/
Computer Languages, AI, NLP

Shubhendu Trivedi: Onionesque Reality
http://onionesquereality.wordpress.com/
Personal Blog, Abstract Ideas, Math, ML, …

Suresh: The Geomblog
http://geomblog.blogspot.com/
Teaching, DataMining, ML, Geometry, Computational Geometry

Terran Lane: Ars Experientia
http://cs.unm.edu/~terran/academic_blog/?m=201201
ML, Teaching

Terry Tao’s Blog
http://terrytao.wordpress.com/
Anything mathematical — deep

I looked through the some machine learning blogs, most of which are listed here, and this is what I found:

Machine Learning Theory – Widely read
http://hunch.net/

Edwin Chen’s blog – Great Visualization, Great Posts
http://blog.echen.me/blog/archives/

Maxim Raginsky’s The Information Structuralist – Some Econ, Some AI
http://infostructuralist.wordpress.com/

Brendan O’Connor’s AI and Social Science blog – Econ, AI, great graphics, fun posts
http://brenocon.com/blog/

Matthew Hurst’s Data Mining: Text Mining, Visualization and Social Media: – Lots of post and images
http://datamining.typepad.com/data_mining/2012/07/index.html

Mikio Braun’s Marginally Interesting MACHINE LEARNING, COMPUTER SCIENCE, JAZZ, AND ALL THAT
http://blog.mikiobraun.de/

Bob Carpenter’s LingPipe Blog Natural Language Processing and Text Analytics – Full of interesting stuff
http://lingpipe-blog.com/

Computer Languages, Matlab, Software, Cool Miscalaneous
http://www.walkingrandomly.com/

Sports Predictions, Lots of other cool ML stuff
http://blog.smellthedata.com/

A personal blog with several AI related posts
http://mybiasedcoin.blogspot.com/

Personal blog with many technical post (some AI)
http://www.daniel-lemire.com/blog/

Three or fewer posts this year:

http://mark.reid.name/iem/
http://blog.smola.org/
http://yaroslavvb.blogspot.com/
http://www.inherentuncertainty.org/
http://justindomke.wordpress.com/
http://nlpers.blogspot.com/
http://www.vetta.org/
http://aicoder.blogspot.com/
http://earningmyturns.blogspot.com/
http://yyue.blogspot.com/

So I decided to look at other AI blogs and I began by typing “artificial intelligence blog” into Google.  (That might not be the best way to find AI blogs, but it seemed like a good place to start.) Most of the links were popular blogs with an article on AI (like the Economist commenting on crossword AI). Here are some of the other links I came across:

A Great List of AI resources
http://www.airesources.info/

Blog – Lisp – 3 posts this year
http://p-cos.blogspot.com/

IEEE on AI
http://spectrum.ieee.org/robotics/artificial-intelligence/

AliceBot
http://alicebot.blogspot.com/

Robotics
http://www.robotcompanions.eu/blog/

Two posts this year – One of the posts is very good.
http://artificialintelligence-notes.blogspot.com/

Good ML blog
http://hunch.net/

This blog markets “Drools” software.
http://blog.athico.com/

Great response to a question about resources for AI.
http://stackoverflow.com/questions/821204/best-books-blogs-link-reading-about-ai-and-machine-learning

Social Cognition and AI
http://artificial-socialcognition.blogspot.com/

AI applied to the stock market
http://thekairosaiproject.blogspot.com/

Carl, I, and the Wikipedia put together this list.

Wikipedia list

• Theory
• • Kolmogorov complexity theory
  • Minimum description length (MDL)
  • Compression
  • Bias-variance dilemma
  • On-line learning methods
  • Cross-validation
  • Ensemble learning
    • Bagging / bootstrapping
    • Boosting
    • Mixture of Experts
  • ROC curve
  • VC dimension
  • PAC learning

• Information theory
  • Noisy-channel coding
  • Entropy
  • Maximum Entropy Method
  • KL distance
  • Compressed Sensing
  • Mutual Information

• Learning
  • Reinforcement Learning
  • Supervised Learning
  • Semi-Supervised Learning
  • Online Learning
  • Multi-Armed Bandit
  • Active Learning
  • Manifold Learning

• Optimization Algorithms
  • Expectation-Maximization
  • Simulated annealing
  • Hill climbing
  • (Stochastic) Gradient Descent
  • Conjugate Gradient
  • Boosting algorithms
  • Branch and bound algorithms
  • Dynamic programming
  • Genetic algorithms
  • Particle swarms
  • A* Search
  • Bayesian Optimization

• Markov Methods & Probabilistic Graphical Models
  • Viterbi / Baum-Welch (algorithms)
  • Hidden Markov models
  • Message Passing, Loopy Belief Propagation
  • Forward-backward algorithm / sum-product algorithm
  • Conditional Random Fields
  • Markov Random Fields
  • Expectation propagation
  • Monte-Carlo Markov chain methods
    • Hastings-Metropolis
    • Gibbs Sampling

• Neural nets
  • Boltzmann machine, Restricted Boltzmann machines
  • Deep Belief Networks (Hinton1, Hinton2, Drop out)
  • Sparse Deep Belief Networks and Autoencoders

• Pattern Recognition
  • Naive Bayes
  • Support vector machines
  • Decision trees (CART)
  • Predictive Analytics
  • Kernel methods (kernelized support vector machine)
  • Kernal Density Estimation (Parzen Windows)
  • Random Forests
  • Structured Prediction
  • Dimension Reduction
    • LDA & QDA (Linear and Quadratic Discriminant Analysis)
    • PCA
    • ICA
    • Self-organizing maps. (Kohonen)
    • Latent Dirichlet Allocation
  • Curve fitting
    • Kriging
    • Linear, Logit, Probit, Non-linear, Lasso, Ridge Regression
    • Locally weighted Regression
    • Generalized Linear Model

• Games
  • Q-learning
  • Upper Confidence Trees
  • Regret Matching
  • Fictitious Play
  • TD-learning
  • Markov Decision Process

• Clustering
  • Mixture of Models (Mixture of Gaussians)
  • K-means
  • K-center
  • Hiearchial (Dendritic) clustering
  • Collaborative Filtering, Singular Value Decomposition

• Misc
  • Variational methods
  • Deconvolution
  • Relevance vector machine (RVM)
  • Regularization