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

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* The difference between an on-line and batch algorithm.
 
* The difference between an on-line and batch algorithm.
* How to implement the voted perceptron.
+
* The perceptron algorithm.
* The definition of a mistake bound, and a margin.
+
* The importance of margins in machine learning.
 
 
 
* The definitions of, and intuitions behind, these concepts:
 
* The definitions of, and intuitions behind, these concepts:
 
** 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 ''primal form'' of the SVM optimization problem.
 
** The ''primal form'' of the SVM optimization problem.
** The ''dual form'' of the SVM optimization problem.
 
** What a ''support vector'' is.
 
** What a ''kernel function'' is.
 
 
** What ''slack variables'' are and why and when they are used in SVMs.
 
** 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 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.
 
* How the perceptron and SVM are similar and different.

Revision as of 18:39, 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.