Difference between revisions of "10-601B Active Learning"
From Cohen Courses
Jump to navigationJump to search (Created page with "This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016 === Slides === * ... === Readings === * ... === What You Should Know === * ...") |
|||
| Line 3: | Line 3: | ||
=== Slides === | === Slides === | ||
| − | * ... | + | * [http://curtis.ml.cmu.edu/w/courses/images/c/c9/AL-2016-post.pdf slides (pdf)] |
| + | * [http://curtis.ml.cmu.edu/w/courses/images/f/f4/AL-2016-post.pptx slides (pptx)] | ||
=== Readings === | === Readings === | ||
| − | * | + | * Two Faces of Active Learning, by Sanjoy Dasgupta (especially sections 1 |
| + | and 2) | ||
| + | |||
| + | * Optional Advanced Readings: | ||
| + | ** Active Learning Literature Survey (by Burr Settles) | ||
| + | ** Active Learning Survey (by Balcan and Urner) | ||
=== What You Should Know === | === 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). | ||
Revision as of 19:46, 13 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)
- 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).
