Tuesday, 06 November 2007 14:44Machine learning is about adaptive thinking.....you don't directly code the solution, instead the computer finds and returns a solution. Typically statistically based and supervised. But unsupervised methods exist. Choose the correct topology and computing methods, feed in your sample data, and ask the computer to generalize the solution according to it's prior training experience.
Backpropagation - Wikipedia - Gradient descent - Wikipedia, newton's method, linear least squares, regression, call it what you will.
O'Reilly Media | Programming Collective Intelligence - a great book about machine learning with code written in python.
Microsoft Solver Foundation - Express Edition - Home - $$ Modeling and solving scenarios by using constraints, goals, and data. Programming in the Optimization Modeling Language (OML), in C# imperatively, in F# functionally, or in any .NET Framework language.
Artificial Intelligence & Machine Learning | Artificial Intelligence , Soft Computing, Machine Learning, Computational Intelligence
Support Vector Machines (SVM) Fundamentals Part-I | Artificial Intelligence & Machine Learning - Support vector machine is a linear discriminant which not only classifies the patterns but also maximizes the margin..
2.7. Mathematical optimization: finding minima of functions — Scipy lecture notes
What do you mean that's undefined?! • How To: Setup a Machine Learning Environment
scikit-learn: machine learning in Python — scikit-learn documentation - Classification,regression,clustering,dimensionality reduction,model selection, preprocessing - Built on NumPy, SciPy, and matplotlib
Realtime Webcam Sudoku Solver - CodeProject - opencv,Hough transformation,OCR,Sudoku solver (brute force,naked single candidates,hidden single)
dartist/sudoku_solver - Solving Every Sudoku Puzzle by Peter Norvig in Dart, Python, C#, Ruby, CoffeeScript
Spaun behaviour and neural simulationworld's largest functional brain model. from Centre for Theoretical Neuroscience(CTN) @ University of Waterloo, Canada
How to build a brain | Nengo
Videos for Spaun simulations | Nengo
Meet Spaun, the Artificial Brain | CaseyRichard.com
Simulated brain scores top test marks : Nature News & Comment
SOMs || Self Organizing Maps || or the shape/relation/structure of things.moritz.stefaner.eu - B.Sc. Thesis - Projection Techniques for Document Maps by Moritz Stefaner
Latent Semantic Indexing using matrix Singular Value Decomposition (SVD) | madAlgorithmist's Blog
madAlgorithmist's Blog | Think out of the 'blog' - awesome blog.
scikit-learn: machine learning in Python — scikit-learn 0.14 documentation - classification, regression, clustering, dimensionality reduction, model selection, feature extraction and normalization. scikit-learn/scikit-learn
Unsupervised Word Segmentation: An Investigation of Sub-word Features? (PDF) - Daniel Blanchard March 20, 2011
Word Segmentation - How do infants come to identify words in the speech stream? dan-blanchard/PHOCUS(Word segmentation framework) dan-blanchard/Phonology-Tools - Tools for working with SPE-style rules
Phonological awareness - Wikipedia - Phonological awareness involves the detection and manipulation of sounds at three levels of sound structure: (1) syllables, (2) onsets and rimes, and (3) phonemes.
Output-Driven Phonology: Theory and Learning - Bruce Tesar - Google Books
The Sounds of Language: An Introduction to Phonetics and Phonology - Elizabeth C. Zsiga - Google Books
Learning Bias and Phonological-Rule Induction (PDF)
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Last Updated on Saturday, 05 July 2014 17:19