Structured Prediction 10-710 in Fall 2011

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Instructor and Venue

Description

This course seeks to cover statistical modeling techniques for discrete, structured data such as text. It brings together content previously covered in Language and Statistics 2 (11-762) and Information Extraction (10-707 and 11-748), and aims to define a canonical set of models and techniques applicable to problems in natural language processing, information extraction, and other application areas. Upon completion, students will have a broad understanding of machine learning techniques for structured outputs, will be able to develop appropriate algorithms for use in new research, and will be able to critically read related literature. The course is organized around methods, with example tasks introduced throughout.

The prerequisite is Machine Learning (10-601 or 10-701), or permission of the instructors.

Syllabus

Older syllabi:

Readings

Unless there's announcement to the contrary, required readings should be done before the class.

Grading

Grades are based on

  • The class project
    • due by 9/8: Choose teams and a general project topic. (This can change in the coming weeks/month.) Create a team wiki page, add its members and the project topic. Every team member then should link to it from their own user homepage.
    • Please see and contribute to Project Brainstorming for 10-710 in Fall 2011.
  • Wiki writeup assignments
  • Class participation

Attendees

Assignment for 9/6: Everyone must make a user home page, as User:USERNAME

People taking this class in Fall 2011 include:

Here are sample pages for William, Noah, and Brendan.

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