10-601B Perceptrons and Large Margin

From Cohen Courses
Revision as of 18:37, 1 February 2016 by Tdick (talk | contribs) (→‎Readings)
Jump to navigationJump to search

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

Slides

Readings

  • The Perceptron Algorithm: Bishop 4.1.7, Mitchell 4.4, Murphy 8.5.4
  • Support Vector Machines: Bishop 7.1

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.
Retrieved from "http://curtis.ml.cmu.edu/w/courses/index.php?title=10-601B_Perceptrons_and_Large_Margin&oldid=18302"