MeritBadges/BasicClassifiers: Difference between revisions
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(New page: == Introduction == Classifiers are a very popular branch of machine learning, with myriad practical applications == Subject Matter Expert == Josh == Requirements == # Dis...) |
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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. |
Latest revision as of 13:54, 11 January 2009
Introduction[edit]
Classifiers are a very popular branch of machine learning, with myriad practical applications
Subject Matter Expert[edit]
Requirements[edit]
- 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[edit]
Bayes Resources[edit]
- 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
- Andrew Moore's Naive Bayes slides A fantastic set of slides, but still missing some details you'd get in the classroom
SVM Resources[edit]
- Kernel trick at wikipedia
- SVM - Support Vector Machines a good collection of graphs showing the separating plane concept
- Andrew Moore's SVM Slides Again, Andrew Moore has excellent slides which provide an overview, but they're sometimes hard to follow.