- Meetings & staff
- Course Expectations & Assignments
- Course policies
- Communication with teaching staff
- Textbooks and resources
Students are expected to be familiar with the basics of machine learning and natural language processing to the extent covered in the corresponding courses in the School of Computing.
Of course, we will introduce some of the relevant prerequisite concepts in the lectures as needed, but knowing the topics will significantly help in understanding the new material.
Course Expectations & Assignments
This is an advanced course that is primarily aimed towards helping you understand recent research to the extent of implementing and using techniques that you may read in papers and, also, hopefully guide you towards becoming a researcher.
Enrolled students are expected to:
Attend class lectures, participate in the class,
Complete readings and assignments.
Present at least one paper from the additional readings in the class and lead discussion around it, and,
Complete a project (in a group at most two students) and submit a report.
There will be no exams.
Your grade is based on the following
- Assignments and reviews (30%),
- Class presentation (15%),
- Project report and presentation (50%), and
- Class participation (5%)
All submissions must be made electronically on Canvas by midnight of the due date. Hand written submissions or printouts will not be accepted.
All assignments must be submitted by the deadline. We will use the timestamp on Canvas as the submission time. Assignments will be accepted up to 24 hours after deadline, but will be assessed a 10% penalty. That is, if your assignment is late and scores 90, then your actual grade will be 81 = 90 - 9. Assignments will not be accepted 24 hours after the deadline.
We will be strict about this policy: If the deadline is midnight and you submit the assignment at 12:01 AM, you will face the 10% penalty!
If there are extraordinary non-academic circumstances, let me know before the deadlines.
No double dipping projects across multiple classes
You can not submit the same project to this class and another class that you may be taking at the same time. If you are doing related projects in two different classes, there may be some overlap (e.g. in code libraries, etc.), but they should not be identical. A project that is found to be double-submitted will receive zero credit. If you have questions about this policy, please contact the instructor.
Collaboration and cheating: Collaboration is encouraged; cheating will not be tolerated.
Important: We will adhere to the academic misconduct policy.
Honor code for this class
You are encouraged to discuss class materials with your peers. If you want you could form study groups because discussions help understanding. You are also welcome to discuss assignments. However, you must write your own solutions, proofs or code and submit your own solution. Do not copy or ask for assignments from other students or the internet. Do not let someone else copy your submissions either.
If you are caught cheating, you will fail the class.
For projects, you are free to discuss the project with anyone in your project group.
For both assignments and projects, you should cite all sources that you refer to. This includes personal communication, books, papers, websites, etc. Doing so reflects academic integrity.
Students with disabilities: The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. Please let me know as soon as possible. If you wish to qualify for exemptions under the Americans with Disabilities Act (ADA), you should also notify the Center for Disability Services.
Also see the College of Engineering guidelines for information about appeals, the Americans with Disabilities Act, repeating courses and add and withdraw deadlines.
Communication with Teaching Staff
We strongly encourage you to post your questions on the class discussion board on Canvas, unless you have a question that you wish to keep confidential. The instructor will monitor the forum and will answer questions. Other students may also be able to answer questions on Canvas.
For grading questions, project related issues or simply a more hands-on interaction, we encourage you to attend the office hours or meet the instructor after class.
Of course, for personal/private questions, do feel free contact the instructor by email or in person.