HMM - Machine Learning - Fall 2018

Three weeks about hidden Markov models as part of the Machine Learning class at the Department of Computer Science, Aarhus University.

Lecutures

Slides are preliminaries until the day of the lecture.

Wednesday, Oct 31: Hidden Markov Models. Terminology and basic algorithms.
Reading material: Bishop section 13.1-13.2 (not 13.2.1, 13.2.4 and 13.2.6).
Supplementary material: See Rabiner for an alternative presetation of HMM methods and applications. See note about graphical models for background about probability theory and terminology.
Slides from lecture:
- HMMs - Terminology, representation and basic problems
- HMMs - Algorithms for decoding
Friday, Nov 2: Hidden Markov Models. Implementing the basic algorithms.
Reading material: Bishop section 13.2.4.
Supplementary reading: You can also take a look at Appendix B - Floating Point Numbers from A. Tanenbaum, Structured Computer Organization, in order to se why we do not want numbers to become too small.
Slides from lecture:
- HMMs - Implementing the forward-, backward-, and Viterbi-algorithms
Wednesday, Nov 7: Hidden Markov Models. Training and selecting model parameters.
Reading material: Bishop section 13.2.1.
Slides from lecture:
- HMMs - Training - Selecting model parameters
Friday, Nov 9: Hidden Markov Models. Selecting the initial model parameter and using HMMs for (simple) gene finding.
Slides from lecture:
- HMMs - Selecting the initial model parameters and using HMMs for (simple) gene finding
Wednesday, Nov 14: Hidden Markov Models. Applications and extensions. Introduction to mandatory project.
Reading material: Bishop 13.2.6.
Slides from lecture:
- HMMs - Hand In 3 comments
Friday, Nov 16: Hidden Markov Models. Some useful extensions.
Reading material: Look at the project description for hand in 3.
Slides from lecture:
- HMMs - Some useful extensions

Exercises

The exercise texts are preliminary until the Friday the week before the exercises are planned.

Week 45 (5/11 - 9/11):
- Theoretical exercises: html, ipynb (the notebook refers to the image graphical-models.png, download it and put it in the same directory as the notebook in order to display it in the notebook)
- Practical exercises: html, ipynb
Week 46 (12/11 - 16/11):
- Theoretical exercises: html, ipynb
- Practical exercises: html, ipynb
Week 47 (19/11 - 23/11):
- Work on "Hand In 3" that must be handed in via Blackboard before November 26, 08:00.
- Hand In 3 as Jupyter notebook
- Hand In 3 as HTML

Material

[Bishop]: Christopher M. Bishop, Pattern Recognition and Machine Learning, Cambridge University Press. (Chapter 13 available via Blackboard.)
[Rabiner]: Lawrence R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceeding of IEEE, 77:257-286, 1989. (Available via Blackboard.)
[Mailund]: Thomas Mailund. Conditional probabilities and graphical models. (Available via Blackboard.)