Computational Linguistics II: Spring 2012

Instructor:

Jordan Boyd-Graber

School:

iSchool at Maryland

Semester:

Spring 2012

Description:

This course builds on Computational Linguistics I. Computational Linguistics I is designed to give students a broad introduction to topics in the field to know what the field is about and why we are interested in these problems. This course, in contrast, goes deeper into the how of specific areas so that students by the end of the course can conduct an independent project in one of these areas and be able to read and understand research papers.

Required Textbook:

Manning, C. D., Schuetze, H. 1999. Foundations of Statistical Natural Language Processing. (Available online)

Link to Syllabus:

http://www.umiacs.umd.edu/~jbg/teaching/CMSC_773_2012/

Computational Linguistics I: Fall 2013

Instructor:

Jordan Boyd-Graber

School:

iSchool at Maryland

Semester:

Fall 2013

Description:

Computers have made it possible, even easy, to collect vast amounts of text from a wide variety of sources. It is not always clear, however, how to use those data and how to extract useful information from data. This problem is faced in a tremendous range of scholarly, government, business, medical, and scientific applications. The purpose of this course is to teach some of the best and most general approaches to get the most out of natural language.

Required Textbook:

No required textbook.

Link to Syllabus:

https://docs.google.com/document/u/2/d/1nTkyPlijzNs0ORk7GXbN2ec4X0eoIT5v655gW2RawgM/pub

Mathematical Foundations of Information Studies: Fall 2014

Instructor:

William Aspray

School:

University of Texas at Austin

Semester:

Fall 2014

Description:

The course has been redesigned and is intended for students who would like a basic mathematical foundation for doing their work as informational professionals, but who have received little or no formal training in mathematics or mathematically oriented disciplines since high school, or whose mathematics is very rusty. The course will focus on five types of mathematical thinking: logical, relational, recursive, quantitative, and analytical. Applications will include ones from big data, social network analysis, modeling of complex systems, and probabilities and statistics.

Required Textbook:

Hunter, D. J. 2012. Essentials of Discrete Mathematics, 2nd ed.

Link to Syllabus:

http://courses.ischool.utexas.edu/bill/2014/Fall/Math%20Syllabus.pdf