Difference between revisions of "10-601B SSL"
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 === * This is not covered in Mitchell. An optional r...") |
|||
| (One intermediate revision by the same user not shown) | |||
| Line 3: | Line 3: | ||
=== Slides === | === Slides === | ||
| − | * ... | + | * [http://www.cs.cmu.edu/~wcohen/10-601/ssl.pdf PDF], [http://www.cs.cmu.edu/~wcohen/10-601/ssl.pptx Powerpoint]. |
=== Readings === | === Readings === | ||
| Line 16: | Line 16: | ||
* The difference between transductive and inductive semi-supervised learning. | * The difference between transductive and inductive semi-supervised learning. | ||
* What graph-based SSL is. | * What graph-based SSL is. | ||
| − | * The definition/implementation of the harmonic function | + | * The definition/implementation of the harmonic function label propagation method. |
Latest revision as of 13:58, 16 March 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- This is not covered in Mitchell. An optional reading is an excellent short textbook by Jerry Zhu: Introduction to Semi-Supervised Learning Synthesis Lectures on Artificial Intelligence and Machine Learning, Chapters 1-3 + 5. This is a free PDF if you're on the CMU network.
Summary
You should know:
- What semi-supervised learning is - i.e., what the inputs and outputs are.
- How K-means and mixture-models can be extended to perform SSL.
- The difference between transductive and inductive semi-supervised learning.
- What graph-based SSL is.
- The definition/implementation of the harmonic function label propagation method.
