- 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 to the extent covered in the machine learning course 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 submit critical reviews in a timely fashion,
Required and additional recommended readings will be assigned for each lecture. The lectures will be based around the readings. You will write short (less than two page) critical reviews of three of the papers in the additional readings. You can select any papers from the ones on the website and submit the reviews before the deadlines listed
Complete other assignments in a timely fashion, 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
- Paper reviews and assignments (45%),
- Project report and presentation (50%), and
- Class participation (5%)
Your assignments must be submitted electronically on Canvas by midnight of the due date. Detailed instructions for submission will accompany each assignment. Hand written assignments or printouts will not be accepted or graded.
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!
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.
School of Computing Policies and Guidelines
Also see the College of Engineering guidelines for information about appeals procedures, withdrawal procedures, and adding and repeating courses.
Collaboration and Cheating
Collaboration is encouraged; cheating will not be tolerated.
You are encouraged to discuss class materials with your peers. If you want you can form study groups because discussions help understanding. You are also welcome to discuss assignments.
However, you must write your own solutions, proofs and 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 once, you will receive a failing grade for that submission. If you are caught cheating again, 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.
The Americans with Disabilities Act
The University of Utah seeks to provide equal access to its programs, services, and activities for people with disabilities. If you will need accommodations in this class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, (801) 581-5020. CDS will work with you and the instructor to make arrangements for accommodations. All written information in this course can be made available in an alternative format with prior notification to the Center for Disability Services.
Personal concerns such as stress, anxiety, relationship difficulties, depression, cross-cultural differences, etc. can interfere with a student’s ability to succeed and thrive at the University of Utah.
For helpful resources, contact the Center for Student Wellness online, or on the phone at 801-581-7776.
University Safety Statement
The University of Utah values the safety of all campus community members. To report suspicious activity or to request a courtesy escort, call campus police at 801-585-COPS (801-585-2677). You will receive important emergency alerts and safety messages regarding campus safety via text message. For more information regarding safety and to view available training resources, including helpful videos, visit safeu.utah.edu.
Addressing Sexual Misconduct
Title IX makes it clear that violence and harassment based on sex and gender (which includes sexual orientation and gender identity/expression) is a civil rights offense subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, color, religion, age, status as a person with a disability, veteran’s status or genetic information.
If you, or someone you know, are harassed or assaulted, you are encouraged to report it to the Title IX Coordinator in the Office of Equal Opportunity and Affirmative Action, 135 Park Building, 801-581-8365, or the Office of the Dean of Students, 270 Union Building, 801-581-7066. For support and confidential consultation, contact the Center for Student Wellness, 426 SSB, 801-581-7776. To report to the police, contact the Department of Public Safety, 801-585-2677(COPS).
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 and the TAs 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, do 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.
Please note that discussion threads and emails are all considered to be equivalent to the classroom, and your behavior in all these venues should conform to the university’s student code.