Difference between revisions of "10-601 SSL"
From Cohen Courses
Jump to navigationJump to search| (10 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] | ||
| + | |||
=== Slides === | === Slides === | ||
| Line 5: | Line 7: | ||
=== Readings === | === Readings === | ||
| − | * This is not covered in Mitchell. An optional reading is an excellent short textbook by Jerry Zhu: [http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006 | + | * This is not covered in Mitchell. An optional reading is an excellent short textbook by Jerry Zhu: [http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006 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. |
| − | 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 === | === 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 SSL method (variously called wvRN, HF, co-EM, ...) | ||
Latest revision as of 16:51, 6 January 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 SSL method (variously called wvRN, HF, co-EM, ...)
