Jbm: /* Resources */

2009-01-11T20:54:07Z

Resources

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	== Resources ==		== Resources ==

			==== Bayes Resources ====


			* [http://www.autonlab.org/tutorials/prob.html Andrew Moore's Probability for Data Miners slides] These are great. They will get you all the probability you need for Naive Bayes, and are very clear/self-contained
			* [http://www.autonlab.org/tutorials/naive.html Andrew Moore's Naive Bayes slides] A fantastic set of slides, but still missing some details you'd get in the classroom

			==== SVM Resources ====
			* [http://en.wikipedia.org/wiki/Kernel_trick Kernel trick] at wikipedia
			* [http://www.dtreg.com/svm.htm SVM - Support Vector Machines] a good collection of graphs showing the separating plane concept
			* [http://www.autonlab.org/tutorials/svm.html Andrew Moore's SVM Slides] Again, Andrew Moore has excellent slides which provide an overview, but they're sometimes hard to follow.

Jbm: New page: == Introduction == Classifiers are a very popular branch of machine learning, with myriad practical applications == Subject Matter Expert == Josh == Requirements == # Dis...

2009-01-11T20:42:45Z

New page: == Introduction == Classifiers are a very popular branch of machine learning, with myriad practical applications == Subject Matter Expert == Josh == Requirements == # Dis...

New page

== Introduction ==

Classifiers are a very popular branch of machine learning, with myriad practical applications

== Subject Matter Expert ==

[[User:jbm|Josh]]

== Requirements ==

# Discuss classifiers, including their inputs, outputs
# Describe the strengths and weaknesses of classifiers
# Demonstrate an understanding of Naive Bayes classifiers
## Describe the idea of conditional probability
## Demonstrate the derivation of Bayes's Theorem
## Explain how Bayes's Theorem is applied to create a Bayesian classifier
## Demonstrate the creation of data structures appropriate for Naive Bayesian classification given a small sample dataset
# Demonstrate an understanding of Support Vector Machines
## Discuss the idea of higher-dimensional feature spaces
## Discuss separability and the challenges it poses
## Discuss separating planes, both verbally and graphically
## Explain the idea and motivation of a maximum margin hyperplane
## Discuss the kernel trick
# Demonstrate practical knowledge. The student will provide a training set and a test set. Then, using one of the above techniques, and training only on the training set, they must achieve 80+% accuracy on the test set.

== Resources ==

MeritBadges/BasicClassifiers - Revision history

Jbm: /* Resources */

Jbm: New page: == Introduction == Classifiers are a very popular branch of machine learning, with myriad practical applications == Subject Matter Expert == Josh == Requirements == # Dis...