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- Why one needs quantitative models: a historical view of
major goofs in other fields.
- Probability theory for Linguists.
- Converting hypotheses about language into mathematical
models.
- Testing models against data.
- Evaluating models for:
- robustness (is it resistant to a small amount of bad
data?),
- stability (does a small change in the data lead to a
small change in the result?), and
- sensitivity (are some data much more important than
others?).
- Choosing and customising machine learning systems so they
match known behaviours of the system.
We will use modern statistical techniques like the Bootstrap
and Jackknife, Markov Chain Monte Carlo, and Monte-Carlo
simulations. We may also touch on Bayesian Belief Networks as a
way of thinking about uncertainty in grammars and other discrete
models. The goal is to help the researcher decide on a good model
and estimate how important a result might be.
The beginning of the course will review probability,
conditional probabilities and Bayes' Theorem. It will also look
at the historical record to study well-documented examples of
pathological science in other fields to look for parallels in
linguistics. Topics discussed may include spam filtering,
attribution of authorship and style, reconstructing language
groups in historical linguistics, and models of stress and
duration, with some attention to perceptual and articulatory
constraints on speech.
The course will be taught primarily via lectures, with class
discussions, and some numerical experiments. A part of the course
will involve discussing research problems that the members are
working on. Calculations will be either accessible on the Web, or
download-able to either Windows or Linux machines. Eight
sessions, 1 hour each.
Prerequisites: General understanding of probability, basic
statistical concepts, algebra. Some programming experience is
valuable, but not essential. Students who have completed Formal
Foundations for Linguistics (S. Pulman) will meet the
prerequisites.
Target Audience: Linguistics and Philology. It could have
application inside Experimental Psychology.