Difference between revisions of "10-601B Perceptrons and Large Margin"
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* [http://curtis.ml.cmu.edu/w/courses/images/d/d2/Perceptron-svm_02_01.pdf Slides in pdf] | * [http://curtis.ml.cmu.edu/w/courses/images/d/d2/Perceptron-svm_02_01.pdf Slides in pdf] | ||
| − | === Readings === | + | === Useful Additional Readings === |
| − | * The Perceptron Algorithm: | + | * The Perceptron Algorithm: Mitchell 4.4.1 & 4.1.2, Bishop 4.1.7 |
| − | * Support Vector Machines: Bishop 7.1 | + | * Support Vector Machines: Bishop 7.1, Murphy 14.5 |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
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** The ''margin'' of a classifier relative to a dataset. | ** The ''margin'' of a classifier relative to a dataset. | ||
** What a ''constrained optimization problem'' is. | ** What a ''constrained optimization problem'' is. | ||
| − | ** The | + | ** The SVM algorithm. |
| − | |||
| − | |||
* How the perceptron and SVM are similar and different. | * How the perceptron and SVM are similar and different. | ||
Latest revision as of 23:36, 8 February 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Useful Additional Readings
- The Perceptron Algorithm: Mitchell 4.4.1 & 4.1.2, Bishop 4.1.7
- Support Vector Machines: Bishop 7.1, Murphy 14.5
What You Should Know Afterward
- The difference between an on-line and batch algorithm.
- The perceptron algorithm.
- The importance of margins in machine learning.
- The definitions of, and intuitions behind, these concepts:
- The margin of a classifier relative to a dataset.
- What a constrained optimization problem is.
- The SVM algorithm.
- How the perceptron and SVM are similar and different.
