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This document is a lecture on hidden Markov models (HMMs) given by Marina Santini at Uppsala University. The lecture covers the basics of HMMs, including Markov assumptions, observation sequences, problems with HMMs, the Viterbi, forward, and backward algorithms, modeling for part-of-speech tagging, learning, smoothing, and inference in HMMs, as well as applications of HMMs. The lecture acknowledges Joakim Nivre for course design and materials.

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- 1. Machine Learning for Language Technology Lecture 7: Hidden Markov Models (HMMs) Marina Santini Department of Linguistics and Philology Uppsala University, Uppsala, Sweden Autumn 2014 Acknowledgement: Thanks to Prof. Joakim Nivre for course design and materials
- 2. Hidden Markov Models (1)
- 3. Hidden Markov Models (2)
- 4. A Simple HMM
- 7. Problems for HMMs (1)
- 8. Problems for HMMs (2)
- 9. Viterbi
- 10. Forward Algo & Backward Algo
- 13. Modeling (1)
- 14. Modeling (2)
- 15. Learning
- 16. Smoothing
- 17. Inference
- 18. HMM Applications
- 20. The end