Difference between revisions of "10-601B Perceptrons and Large Margin"

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=== Readings ===
 
=== Readings ===
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* [http://www.cs.cmu.edu/~wcohen/10-601/vp-notes/vp.pdf My notes on the voted perceptron].  (You can skip sections 3-4 on ranking and the structured perceptron).
 
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* Optional reading:  Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.
 
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* Optional background on linear algebra: [http://www.cs.cmu.edu/~zkolter/course/linalg/ Zico Kolter's linear algebra review lectures]
 
  
 
=== What You Should Know Afterward ===
 
=== What You Should Know Afterward ===

Revision as of 18:22, 1 February 2016

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

Readings

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 primal form of the SVM optimization problem.
    • What slack variables are and why and when they are used in SVMs.
  • How to explain the different parts (constraints, optimization criteria) of the primal and dual forms for the SVM.
  • How the perceptron and SVM are similar and different.
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