Steve Abney – University of Michigan
Course time: Monday/Wednesday 11:00 am – 12:50 pm
1401 Mason Hall
This course provides a general introduction to machine learning. Unlike results in learnability, which are very abstract and have limited practical consequences, machine learning methods are eminently practical, and provide detailed understanding of the space of possibilities for human language learning.
Machine learning has come to dominate the field of computational linguistics: virtually every problem of language processing is treated as a learning problem. Machine learning is also making inroads into mainstream linguistics, particularly in the area of phonology. Stochastic Optimality Theory and the use of maximum entropy models for phonotactics may be cited as two examples.
The course will focus on giving a general understanding of how machine learning methods work, in a way that is accessible to linguistics students. There will be some discussion of software, but the focus will be on understanding what the software is doing, not in the details of using a particular package.
The topics to be touched on include classification methods (Naive Bayes, the perceptron, support vector machines, boosting, decision trees, maximum entropy classifiers) and clustering (hierarchical clustering, k-means clustering, the EM algorithm, latent semantic indexing), sequential models (Hidden Markov Models, conditional random fields) and grammatical inference (probabilistic context-free grammars, distributional learning), semisupervised learning (self-training, co-training, spectral methods) and reinforcement learning.