Difference between revisions of "10-601B Perceptrons and Large Margin"
From Cohen Courses
Jump to navigationJump to search| Line 14: | Line 14: | ||
* The difference between an on-line and batch algorithm. | * The difference between an on-line and batch algorithm. | ||
| − | * | + | * The perceptron algorithm. |
| − | * The | + | * 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. | ||
| − | |||
| − | |||
| − | |||
** 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 17:39, 1 February 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- My notes on the voted perceptron. (You can skip sections 3-4 on ranking and the structured perceptron).
- Optional reading: Freund, Yoav, and Robert E. Schapire. "Large margin classification using the perceptron algorithm." Machine learning 37.3 (1999): 277-296.
- Optional background on linear algebra: Zico Kolter's linear algebra review lectures
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.
