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 11:08, 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).