Course Info for Ling647/CMSC828R, Spring 2005

Course Info for Ling647/CMSC828R, Spring 2005
Computational Linguistics II

Class mailing list

Students should be on the class mailing list. Messages are archived on a weekly basis.

Essentials

Instructor:
Philip Resnik.
Location and time of the class:
Computer Science Instructional Center (CSI) Room 2107, Wednesday, 4-6:30pm.
Text:
Weekly readings listed in the schedule of topics, mostly from Christopher D. Manning and Hinrich Schuetze, Foundations of Statistical Natural Language Processing.
Syllabus:
See the schedule of topics.
Office hours:
By appointment.

What's the course about?

This is the second semester in our new graduate sequence in computational linguistics. Students are assumed to have taken the first semester (Ling645/CMSC723) or equivalent, and this class will provide foundations for advanced seminars in computational linguistics. Students are expected to be able to know how to program, and will exercise this ability periodically in homework assignments.

The topics we'll cover are intended to get students up to speed on necessary background in order to understand and perform cutting-edge research in natural language processing, which requires a strong grounding in statistical NLP models and methods. The syllabus is not yet 100% finalized, but it will follow Manning and Schuetze's textbook relatively closely at least in early parts of the course.

Please feel free to contact me with any questions, and feel free to suggest the course to anyone who might be interested.

See the schedule of topics for class-by-class plans.

How will the course be graded?:

Students will be evaluated on their ability to master the content of the material in the course and to think critically about ideas presented to them.

Exams (50%): There will be a midterm exam and a final exam. CS MS comp grade (AI area) will be based entirely on the average of the two exams.
Class assignments/projects (40%): There will be periodic homework assignments that may involve on-paper exercises (e.g. walking through algorithms or calculations), hands-on programming, or analysis of data.
Class participation (10%): Showing up for class, demonstrating preparedness, and contributing to class discussions.

Policy for Incomplete Work

Late assignments. If an assignment is late by up to 24 hours, the grade will be reduced by 10%. By 48 hours, 20%. And so forth. Exceptions can be discussed in cases of medical excuses, family emergencies, equipment failure, etc., but being busy is not a valid excuse, and the sooner you talk to me about a problem the better.
There are several common problems I am unlikely to consider as valid reasons for failing to get work in on time. These include (a) failure to manage your time properly, (b) discovering an assignment is harder than you expected it to be (see item a), and (c) losing code or data that should have been backed up, unless it's someone else's fault.
Late assignment exception. Each student can ask me to extend an assignment due date by 48 hours one time during the semester.
'Incomplete' as a grade. I will not issue an 'incomplete' as a grade except for serious, valid reasons, generally in the category of emergencies. See above for some reasons I am unlikely to consider valid. If you are having problems of any kind, please talk to me as soon as possible.