Machine Learning: Links, News and Resources (1)

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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
Enroll today in our online class for free!

Reinforcement Learning: An Introduction

Machine Learning
Systems that Improve Their Performance

Does Machine Learning Really Work?

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

TD Gammon

University of Alberta, Department of Computing Science, Machine Learning

University of Alberta, CS, Research

Turing award goes to ‘machine learning’ expert

Stanford School of Engineering – Stanford Engineering Everywhere

InfoQ: Machine Learning: A Love Story

Informaniac: Machine Learning for Bug Discovery

io9. We come from the future.

bradford’s infer at master – GitHub

Pragmatic Programming Techniques: Map/Reduce to recommend people connection

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

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

Apache Mahout – Overview

Papers – Hadoop Wiki

20Q – Wikipedia, the free encyclopedia

Machine Learning in Game AI – Stack Overflow

Applications of Machine Learning to the Game of Go
David Stern, Applied Games Group, Microsoft Research Cambridge

Deep Boltzmann Machine on MNIST

Introduction to MGL (part 1)

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

IET/BCS Turing Lecture 2010 | Professor Christopher Bishop

So you think machine learning is boring?

Google AI Challenge

Common Lisp and Google AI Challenge

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

Infer.NET – Now with F# support @

Pragmatic Programming Techniques: Machine Learning: Association Rule

Pragmatic Programming Techniques: Machine Learning with Linear Model

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

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

Introducing Apache Mahout

Apache Mahout – Overview

How Flightcaster squeezed predictions from flight data

Map-Reduce for Machine Learning on Multicore

Torch3: The Dream Comes True

Reinforcement Learning and Artificial Intelligence: Toolkit

Machine Learning Book Code

Scientific Commons: Simon Colton

A Grid-based Application of machine learning to model generation

Reinforcement Learning: An Introduction

Learning Draughts/Checkers

Learning Connect Four

Introduction to Machine Learning

The Use of Java in Machine Learning

Similarity Learning – IDL – EE –


Machine Learning

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

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

My Links

Angel “IAmStillLearning” Lopez

One thought on “Machine Learning: Links, News and Resources (1)

  1. Pingback: Machine Learning: Links, News And Resources (2) | Angel "Java" Lopez on Blog

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