Machine Learning: Links, News and Resources (1)

http://en.wikipedia.org/wiki/Machine_learning

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Machine learning is concerned with the development of algorithms allowing the machine to learn via inductive inference based on observing data that represents incomplete information about statistical phenomenon and generalize it to rules and make predictions on missing attributes or future data. An important task of machine learning is classification, which is also referred to as pattern recognition, in which machines “learn” to automatically recognize complex patterns, to distinguish between exemplars based on their different patterns, and to make intelligent predictions on their class.

Machine Learning at Stanford

http://www.ml-class.org/course/auth/welcome

Enroll today in our online class for free!

Reinforcement Learning: An Introduction

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Machine Learning
Systems that Improve Their Performance

http://aaai.org/AITopics/MachineLearning

Does Machine Learning Really Work?

http://www.aaai.org/ojs/index.php/aimagazine/article/view/1303/1204

My first encounter with the topic:
Samuel’s Checkers Player

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node109.html

TD Gammon

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node108.html

University of Alberta, Department of Computing Science, Machine Learning

https://www.cs.ualberta.ca/research/research-areas/machine-learning

University of Alberta, CS, Research

https://www.cs.ualberta.ca/research

See

https://www.cs.ualberta.ca/research/research-areas/bioinformatics


https://www.cs.ualberta.ca/research/research-areas/computer-games


https://www.cs.ualberta.ca/research/research-areas/artificial-intelligence


https://www.cs.ualberta.ca/research/research-areas/advanced-man-machine-interfaces

Turing award goes to ‘machine learning’ expert

http://www.physorg.com/news/2011-03-turing-award-machine-expert.html

Stanford School of Engineering – Stanford Engineering Everywhere

http://see.stanford.edu/see/courseInfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

InfoQ: Machine Learning: A Love Story

http://www.infoq.com/presentations/Machine-Learning

Informaniac: Machine Learning for Bug Discovery

http://www.informaniac.net/2008/06/machine-learning-for-bug-discovery.html

io9. We come from the future.

http://m.io9.com/5659503/a-computer-learns-the-hard-way-by-reading-the-internet

bradford’s infer at master – GitHub

http://github.com/bradford/infer

Pragmatic Programming Techniques: Map/Reduce to recommend people connection

http://horicky.blogspot.com/2010/08/mapreduce-to-recommend-people.html

Smarter Than You Think – I.B.M.’s Supercomputer to Challenge ‘Jeopardy!’ Champions – NYTimes.com

http://www.nytimes.com/2010/06/20/magazine/20Computer-t.html?hp

Google Prediction API: Commoditization of Large-Scale Machine Learning? – A Computer Scientist in a Business School

http://behind-the-enemy-lines.blogspot.com/2010/05/google-prediction-api-commoditization.html

Apache Mahout – Overview

http://mahout.apache.org/

Papers – Hadoop Wiki

http://wiki.apache.org/hadoop/Papers

20Q – Wikipedia, the free encyclopedia

http://en.wikipedia.org/wiki/20Q

Machine Learning in Game AI – Stack Overflow

http://stackoverflow.com/questions/970060/machine-learning-in-game-ai

Applications of Machine Learning to the Game of Go

http://videolectures.net/epsrcws08_stern_aml/

David Stern, Applied Games Group, Microsoft Research Cambridge

Deep Boltzmann Machine on MNIST

http://quotenil.com/Deep-Boltzmann-Machine-on-MNIST.html

Introduction to MGL (part 1)

http://quotenil.com/Introduction-to-MGL-(part-1).html

Measuring Measures – blog – Learning about Machine Learning, 2nd Ed.

http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html

IET/BCS Turing Lecture 2010 | Professor Christopher Bishop

http://tv.theiet.org/technology/infopro/turing-2010.cfm

So you think machine learning is boring?

http://www.causata.com/blog/2010/02/so-you-think-machine-learning-is-boring.html

Google AI Challenge

http://csclub.uwaterloo.ca/contest/

Common Lisp and Google AI Challenge

http://aerique.blogspot.com/2010/02/google-ai-challenge-2010.html

Infer.NET: Building Software with Intelligence :: Sessions :: Microsoft PDC09

http://microsoftpdc.com/Sessions/VTL03

Infer.NET – Now with F# support @ JustinLee.sg

http://www.justinlee.sg/2009/12/09/infer-net-now-with-f-support/

Pragmatic Programming Techniques: Machine Learning: Association Rule

http://horicky.blogspot.com/2009/10/machine-learning-association-rule.html

Pragmatic Programming Techniques: Machine Learning with Linear Model

http://horicky.blogspot.com/2009/11/machine-learning-with-linear-model.html

A New Theory of Awesomeness and Miracles, by James Bridle, concerning Charles Babbage, Heath Robinson, MENACE and MAGE

http://shorttermmemoryloss.com/menace/

A small personal project to learn Clojure by implementing some simple machine learning algorithms edit

http://github.com/mreid/injuce/

Introducing Apache Mahout

http://www.ibm.com/developerworks/java/library/j-mahout/index.html

Apache Mahout – Overview

http://lucene.apache.org/mahout/

How Flightcaster squeezed predictions from flight data

http://www.datawrangling.com/how-flightcaster-squeezes-predictions-from-flight-data

Map-Reduce for Machine Learning on Multicore

http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf

Torch3: The Dream Comes True

http://www.torch.ch/introduction.php

Reinforcement Learning and Artificial Intelligence: Toolkit

http://rlai.cs.ualberta.ca/RLAI/RLtoolkit/RLtoolkit1.0.html

Machine Learning Book Code

http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html

Scientific Commons: Simon Colton

http://de.scientificcommons.org/simon_colton

A Grid-based Application of machine learning to model generation

http://www.doc.ic.ac.uk/~sgc/papers/sorge_ki04.pdf

Reinforcement Learning: An Introduction

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Learning Draughts/Checkers

http://www.codeproject.com/KB/game/learning_draughts.aspx

Learning Connect Four

http://www.codeproject.com/KB/game/learningconnectfour.aspx

Introduction to Machine Learning

http://robotics.stanford.edu/~nilsson/mlbook.html

The Use of Java in Machine Learning

http://www.developer.com/java/other/article.php/10936_1559871_1

Similarity Learning – IDL – EE – Washington.edu

http://idl.ee.washington.edu/similaritylearning/

Gwap

http://www.gwap.com/gwap/about/

Machine Learning

http://aima.cs.berkeley.edu/ai.html#learning

Artificial Intelligence on the Web

http://aima.cs.berkeley.edu/ai.html

This page links to 820 pages around the web with information on Artificial Intelligence.

Yu-Han Chang

http://www.yuhanchang.com/home.html

“My research centers on learning in rich multi-agent environments”

My Links

http://www.delicious.com/ajlopez/machinelearning

Angel “IAmStillLearning” Lopez

http://www.ajlopez.com


http://twitter.com/ajlopez

Lambda Calculus: Links, News and Resources (1)

"Everything is a lambda in the end" ajlopez (past century)

http://en.wikipedia.org/wiki/Lambda_calculus

In mathematical logic and computer science, lambda calculus, also written as λ-calculus, is a formal system for function definition, function application and recursion. The portion of lambda calculus relevant to computation is now called the untyped lambda calculus. In both typed and untyped versions, ideas from lambda calculus have found application in the fields of logic, recursion theory (computability), and linguistics, and have played an important role in the development of the theory of programming languages (with untyped lambda calculus being the original inspiration for functional programming, in particular Lisp, and typed lambda calculi serving as the foundation for modern type systems).

Closures + Lambda = The key to OOP in Lisp « Learning Lisp

http://lispy.wordpress.com/2007/07/18/closures-lambda-the-key-to-oop-in-lisp/

Papers | Lambda the Ultimate

http://lambda-the-ultimate.org/papers

Notas sobre el Cálculo Lambda

http://ajlopez.zoomblog.com/archivo/2009/04/14/notas-sobre-el-Calculo-Lambda.html

Presenting AjLambda, lambda calculus in C#

http://ajlopez.wordpress.com/2009/02/25/presenting-ajlambda-lambda-calculus-implementation-in-c/

Funarg problem

http://en.wikipedia.org/wiki/Funarg_problem

The lambda calculus

http://faculty.knox.edu/dblaheta/research/lambda.pdf

Fixed point combinator

http://en.wikipedia.org/wiki/Y-combinator

Introducction to Lambda Calculus

http://www.cs.chalmers.se/Cs/Research/Logic/TypesSS05/Extra/geuvers.pdf

Introduction to Lambda Calculus

http://citeseer.ist.psu.edu/barendregt94introduction.html

Lambda Calculus

http://www.cs.bham.ac.uk/~axj/pub/papers/lambda-calculus.pdf

A Tutorial Introduction to the Lambda Calculus

http://www.utdallas.edu/~gupta/courses/apl/lambda.pdf

Knights of the Lambda Calculus

http://en.wikipedia.org/wiki/Knights_of_the_Lambda_Calculus

A Hacker’s Introduction to Partial Evaluation | The Lambda meme – all things Lisp, and more

http://www.ymeme.com/hackers-introduction-partial-evaluation.html

To Dissect a Mockingbird: A Graphical Notation for the Lambda Calculus with Animated Reduction

http://users.bigpond.net.au/d.keenan/Lambda/index.htm

http://www.defmacro.org/

λProlog Home Page

http://www.lix.polytechnique.fr/Labo/Dale.Miller/lProlog/

Church’s Type Theory

http://plato.stanford.edu/entries/type-theory-church/

Lambda Calculus Schemata

http://cs-www.cs.yale.edu/homes/fischer/pubs/lambda.pdf

Lambda Animator : animated reduction of the lambda calculus

http://thyer.name/lambda-animator/

Peter Selinger: Papers

http://www.mscs.dal.ca/~selinger/papers.html

Mike Taulty’s Blog : Anonymous Methods, Lambdas, Confusion

http://mtaulty.com/CommunityServer/blogs/mike_taultys_blog/archive/2009/01/28/anonymous-methods-lambdas-confusion.aspx

Lecture Notes on the Lambda Calculus (pdf)

http://www.mscs.dal.ca/~selinger/papers/lambdanotes.pdf

A Security Kernel Based on the Lambda-Calculus

http://mumble.net/~jar/pubs/secureos/

System F: Second-order lambda calculus

http://en.wikipedia.org/wiki/System_F

(Mis)using C# 4.0 Dynamic – Type-Free Lambda Calculus, Church Numerals, and more

http://community.bartdesmet.net/blogs/bart/archive/2009/08/17/mis-using-c-4-0-dynamic-type-free-lambda-calculus-church-numerals-and-more.aspx

Type-Free Lambda Calculus in C#, Pre-4.0 – Defining the Lambda Language Runtime (LLR)

http://community.bartdesmet.net/blogs/bart/archive/2009/08/30/type-free-lambda-calculus-in-c-pre-4-0-defining-the-lambda-language-runtime-llr.aspx

Jim McBeath: Practical Church Numerals in Scala

http://jim-mcbeath.blogspot.com/2008/11/practical-church-numerals-in-scala.html

My Links

http://www.delicious.com/ajlopez/lambda

Angel "AjLambda" Lopez