Difference between revisions of "10-601B Perceptrons and Large Margin"
From Cohen Courses
Jump to navigationJump to search (→Slides) |
|||
| Line 7: | Line 7: | ||
=== Readings === | === 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 === | === What You Should Know Afterward === | ||
Revision as of 18:37, 1 February 2016
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.
