While there are no formal prerequisites, this is an advanced topic course aimed at graduate students doing research in natural language processing, machine learning and related areas. We will assume that students are comfortable with the fundamentals of machine learning, deep neural networks, and natural language processing (e.g., CMSC723, CMSC470), or that they will catch up on their own. An early exam will help students assess their degree of preparation.
Recommended reference material:
There is no required textbook. All readings will be selected from resources that are available online freely for UMD students.
We will use Canvas for all assignments and course communications. If you are registered student, please contact instructors via Canvas rather than email.
Your grade will be based on:
participation (10%)
in class presentations (20%)
research project to be done in groups of up to 4 students (70%). Deliverables will include a project pitch (5%), a project proposal (with literature review, proposed approach and evaluation; 15%), a short update on main results (10%), and a final write-up and presentation (40%).
Attendance and participation is important for a seminar course. Any student who needs to be excused for a prolonged absence (2 or more consecutive class meetings), or for a Major Scheduled Grading Event, must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities. In addition, it must contain the name and phone number of the medical service provider to be used if verification is needed. No diagnostic information will ever be requested. The Major Scheduled Grading Events for this course include: quizz, language in 10 minutes presentations, paper presentations, project checkpoint presentations.
Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Office of Disability Support Services (DSS) within the first TWO weeks of the semester.
In this course you are responsible for both the University’s Code of Academic Integrity and the University of Maryland Guidelines for Acceptable Use of Computing Resources. Any evidence of unacceptable use of computer accounts or unauthorized cooperation on tests and assignments will be submitted to the Student Honor Council, which could result in an XF for the course, suspension, or expulsion from the University.
Note that posting project solutions in a public online location is a violation of your academic integrity policy.
Any homework or exam that is handed in must be your own work. However, talking with one another to understand the material better is strongly encouraged. Recognizing the distinction between cheating and cooperation is very important. If you copy someone else’s solution, you are cheating. If you let someone else copy your solution, you are cheating. If someone dictates a solution to you, you are cheating. Everything you hand in must be in your own words, and based on your own understanding of the solution. If someone helps you understand the problem during a high-level discussion, you are not cheating. We strongly encourage students to help one another understand the material presented in class, in the book, and general issues relevant to the assignments. When taking an exam, you must work independently. Any collaboration during an exam will be considered cheating. Any student who is caught cheating will be given an E in the course and referred to the University Student Behavior Committee. Please don’t take that chance - if you’re having trouble understanding the material, please let us know and we will be more than happy to help.
The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the aims and goals of this course. These require a community and an environment that recognizes the inherent worth of every person and group, that fosters dignity, understanding, and mutual respect, and that embraces diversity. Harassment and hostile behavior are unwelcome in any part of this course. This includes: speech or behavior that intimidates, creates discomfort, or interferes with a person’s participation or opportunity for participation in the course. We aim for this course to be an environment where harassment in any form does not happen, including but not limited to: harassment based on race, gender, religion, age, color, national origin, ancestry, disability, sexual orientation, or gender identity. Harassment includes degrading verbal comments, deliberate intimidation, stalking, harassing photography or recording, inappropriate physical contact, and unwelcome sexual attention. Please contact an instructor or CS staff member if you have questions or if you feel you are the victim of harassment (or otherwise witness harassment of others).
We welcome your suggestions for improving this class, please don’t hesitate to share it with the instructor or the TA during the semester! You will also be asked to give feedback using the CourseEvalUM system at the end of the semester. Your feedback will help us make the course better.
Although every effort has been made to be complete and accurate, unforeseen circumstances arising during the semester could require the adjustment of any material given here. Consequently, given due notice to students, the instructor reserves the right to change any information on this syllabus or in other course materials.