Teaching: Difference between revisions
Computational Linguistics and Information Processing
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figure prominently. The seminar will combine reviewing fundamental material, taking a reading-group | figure prominently. The seminar will combine reviewing fundamental material, taking a reading-group | ||
approach to key advanced papers, and (hopefully) bringing in guest speakers. Cross-disciplinary | approach to key advanced papers, and (hopefully) bringing in guest speakers. Cross-disciplinary | ||
participation is strongly encouraged. | participation is strongly encouraged. ([http://umiacs.umd.edu/~resnik/ling848_fa2010/ Course Web page]) | ||
== Machine Learning (Getoor) == | == Machine Learning (Getoor) == | ||
Revision as of 00:31, 28 October 2010
Fall 2010
Computational Linguistics I (Daume)
Fundamental methods in natural language processing. Topics include: finite-state methods, context-free and extended context-free models of syntax; parsing and semantic interpretation; n-gram and Hidden Markov models, part-of-speech tagging; natural language applications such as machine translation, automatic summarization, and question answering.
Seminar in Computational Linguistics (Resnik)
This advanced seminar will focus on computational modeling of language, including cognitive/linguistic aspects as well as practical language technology. Bayesian and information theoretic approaches will figure prominently. The seminar will combine reviewing fundamental material, taking a reading-group approach to key advanced papers, and (hopefully) bringing in guest speakers. Cross-disciplinary participation is strongly encouraged. (Course Web page)
Machine Learning (Getoor)
Reviews and analyzes both traditional symbol-processing methods and genetic algorithms as approaches to machine learning. (Neural network learning methods are primarily covered in CMSC 727.) Topics include induction of decision trees and rules, version spaces, candidate elimination algorithm, exemplar-based learning, genetic algorithms, evolution under artificial selection of problem-solving algorithms, system assessment, comparative studies, and related topics.