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 === * ...") |
|||
(2 intermediate revisions by the same user not shown) | |||
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) [http://cseweb.ucsd.edu/~dasgupta/papers/twoface.pdf (link)] |
+ | |||
+ | * 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). |
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).