# 10-601 SVMS

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Jump to navigationJump to searchThis a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

### Slides

### Readings

### Assignment

- None

### What You Should Know Afterward

- 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. - 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.

- The
- 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.