Difference between revisions of "10-601B Active Learning"
From Cohen Courses
Jump to navigationJump to search| (One intermediate revision by the same user not shown) | |||
| Line 8: | Line 8: | ||
=== Readings === | === Readings === | ||
| − | * Two Faces of Active Learning, by Sanjoy Dasgupta (especially sections 1 | + | * Two Faces of Active Learning, by Sanjoy Dasgupta (especially sections 1 and 2) [http://cseweb.ucsd.edu/~dasgupta/papers/twoface.pdf (link)] |
| − | and 2) | ||
* Optional Advanced Readings: | * Optional Advanced Readings: | ||
Latest revision as of 10:08, 14 March 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Two Faces of Active Learning, by Sanjoy Dasgupta (especially sections 1 and 2) (link)
- Optional Advanced Readings:
- Active Learning Literature Survey (by Burr Settles)
- Active Learning Survey (by Balcan and Urner)
What You Should Know
- What is active learning:
- Batch Active Learning
- Selective Sampling and Active Learning
- Active learning could provide exponential improvements in label complexity (both theoretically and practically)!
- Common heuristics (e.g., those based on uncertainty sampling).
- Sampling bias.
- Safe Disagreement Based Active Learning Schemes.
- Understand how they operate precisely in the realizable case (noise free scenarios).
