Difference between revisions of "10-601 Sequences"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/hmms.pptx Slides in PowerPoint]. | + | * [http://www.cs.cmu.edu/~wcohen/10-601/hmms.pptx Slides in PowerPoint], [http://www.cs.cmu.edu/~wcohen/10-601/hmms.pdf in PDF] |
| − | === Readings === | + | === Optional Readings === |
| − | * This is not covered in Mitchell. | + | * This is not covered in Mitchell. For HMMS: Bishop 13.1-13.2 cover this material. The most-used introduction to HMMs is: ''Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286.'' There is a nice [http://arxiv.org/abs/1011.4088 general introduction to CRFs] by Sutton and McCallum. |
=== Summary === | === Summary === | ||
Latest revision as of 15:16, 21 April 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Optional Readings
- This is not covered in Mitchell. For HMMS: Bishop 13.1-13.2 cover this material. The most-used introduction to HMMs is: Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77.2 (1989): 257-286. There is a nice general introduction to CRFs by Sutton and McCallum.
Summary
You should know:
- The definition of an HMM
- What the Viterbi and forward-background algorithms are:
- What their complexity is
- What they compute.
- How to learn the parameters HMMs when
- The states associated with the training data are observed
- The states are unobserved.
- What the advantages of a CRF are compared to an HMM.
- How HMMs and CRFs relate to naive Bayes, logistic regression, and generative and discriminative models.
- How HMMs or CRFs can be used for named-entity recognition (NER) and other sequential classification tasks.
