Difference between revisions of "10-601B Perceptrons and Large Margin"
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=== Slides === | === Slides === | ||
| − | * | + | * [http://curtis.ml.cmu.edu/w/courses/images/8/82/Perceptron-svm_02_01.pptx Slides in powerpoint] |
| + | * [http://curtis.ml.cmu.edu/w/courses/images/d/d2/Perceptron-svm_02_01.pdf Slides in pdf] | ||
=== Readings === | === Readings === | ||
Revision as of 18:25, 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.
