» Tutorials http://dataminingtools.net/blog Mon, 25 Jul 2011 08:51:53 +0000 http://wordpress.org/?v=2.9.2 en hourly 1 Online Data Mining Reference Book http://dataminingtools.net/blog/2010/12/01/online-data-mining-reference-book/ http://dataminingtools.net/blog/2010/12/01/online-data-mining-reference-book/#comments Wed, 01 Dec 2010 19:54:09 +0000 Vikramaditya Jakkula http://dataminingtools.net/blog/?p=655

The online book was created by The Data Mining group was established in November 2000 by Dr. Saed Sayad in a collaboration with Professor Stephen T. Balke in the Department of Chemical Engineering and Applied Chemistry at the University of Toronto.

You can visit the group here:

http://chem-eng.utoronto.ca/~datamining/

You can access the book here:

http://chem-eng.utoronto.ca/~datamining/dmc/data_mining_map.htm

Features:

  • Quick Reference
  • Easy to memorize
  • Neat layout with good colored illustrations
  • Easy navigation
  • and more.

Happy Reading!

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]]> http://dataminingtools.net/blog/2010/12/01/online-data-mining-reference-book/feed/ 0 Machine Learning at Stanford University http://dataminingtools.net/blog/2010/03/28/open-education-on-machine-learning-by-stanford-university/ http://dataminingtools.net/blog/2010/03/28/open-education-on-machine-learning-by-stanford-university/#comments Sun, 28 Mar 2010 11:34:09 +0000 vinayak http://dataminingtools.net/blog/?p=504 This time we bring to you an online video tutorial on CS 229: Machine Learning course from Stanford University. Besides 19 Lectures on machine learning you will be provided with relevant lecture notes, links other related miscellaneous stuff . Professor Andrew Ng will introduce  machine learning and statistical pattern recognition which will cover topics like supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Other applications of machine learning, such as to robotic control, data mining, autonomous navigation, bio informatics, speech recognition, and text and web data processing are also discussed.

This was the first lecture:- (course materials) The first 30 minutes or so of this lecture is introduction to the course and the field of machine learning in general.

  • In the rest of the lecture, the four main parts of the course is described in some detail along with illustrative examples :-Supervised learning: providing the algorithm a data set, supervising- learn the association between input & output, regression problems, classification problems, support vector machines- infinite number of features
  • Learning theory
  • Unsupervised learning: clustering, Cocktail party problem, independent component analysis Reinforcement learning: reward function (good dog, bad dog), feedback function

In the second lecture, Professor Andrew Ng will cover topics like linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

For more  visit the home page: http://cs229.stanford.edu/

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