CS 6741

Course information provided by the Courses of Study 2016-2017.

Robust language understanding has the potential to transform how we interact with computers, extract information from text and study language on large scale. However, to accurately recover the meaning of language, automated systems must learn to reason about the meaning of words and the intricate structures they combine to. This research-oriented course examines machine learning and inference methods for recovering structured representations of language meaning. Possible topics include formalisms, inference and learning for: sequence models (tagging, named-entity recognition), tree models (constituency and dependency parsing), mapping sentences to logical form representations and alignment models (machine translation).

When Offered Fall.

Permission Note Enrollment limited to: Ph.D. students.
Prerequisites/Corequisites Prerequisite: CS 2110 or equivalent programming experience, a course in machine learning (CS 4780/CS 5780, CS 6780 or equivalent).

View Enrollment Information

  • Enrollment Information
    Combined with: CS 6741

  • 3 Credits Graded

  • Enrollment Information
    Combined with: CS 6741

  • 3 Credits Graded

  • 13133CS 6741  LEC 030

  • Taught in NYC. Enrollment limited to: Cornell Tech PhD students.