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This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
- 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.
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, ...)