Contents
Grades
Course grades will be calculated according to the following weighted average.
| Category | Weight | ||||||
|---|---|---|---|---|---|---|---|
| Final Project Site | 25% | ||||||
| Technical Demo | 20% | ||||||
| Individual Assessments | 20% | ||||||
| Homeworks | 10% | ||||||
| Class Participation | 10% | ||||||
| Proposals | 10% | ||||||
| Presentations | 5% |
Course grades will be converted to letters according to the following. There is no curve, and there is no rounding.
| Letter Grade | Needs at least | Letter Grade | Needs at least |
|---|---|---|---|
| A+ | 97 | C+ | 77 |
| A | 93 | C | 73 |
| A- | 90 | C- | 70 |
| B+ | 87 | D+ | 67 |
| B | 83 | D | 63 |
| B- | 80 | D- | 60 |
The following meta-rubric details how you should generally expect individual assignments to be graded.
| Meta-Rubric Category | Numerical Score | Notes |
|---|---|---|
| E: Excellent | 100% | Above and beyond, insightful, clear, extensive. |
| G: Good | 93% | Well-done, consistent and coherent. |
| S: Satisfactory | 80% | Meets basic expectations, accomplishes essential goals. |
| U: Unsatisfactory | 60% | Complete but incorrect or unclear, ineffective or superficial. |
| I: Incomplete | 40% | Substantial but incomplete attempt. |
| N: Not Attempted | 0% | No or insubstantial attempt. |
Assignment Submission, Lateness, and Regrades
All assignments will be submitted on Gradescope, accessible directly or through Sakai. Assignments will be accepted late for a penalty of up to 10% grade reduction per 24 hours. No assignments will be accepted after the last day of classes.
Grades and feedback will also be marked and returned on Gradescope. You have the right to request a regrade if you feel the grade marked does not correctly represent the quality of your work or has been judged unfairly. If your original grade was assigned by a teaching assistant, the regrade request will be considered by the instructor. Regrade requests must be made in writing on Gradescope within one week of the return of an assignment grade, and should reference the rubric and the specifics of the work submitted, not simply ask for more credit. A regraded request may result in an assignment grade increasing, staying the same, or decreasing.
Attendance, Participation, Illness, and Absence
In-person attendance is expected, and attendance will be taken in class. Class will be recorded for your reference or if you miss a class, but there is no online option for participation like zooming into class. Most classes will include some time for active participation, typically in the form of group discussion, in addition to lecture. You should be prepared to engage during class, and should put away distractions such as other homework, emails, etc. Note that attendance and participation constitute a portion of the course grade.
There are times you cannot make it to class for any number of reasons. I fully support your observation of religious holidays, the occasional need for personal travel, physical illness (see below), mental health, and other reasons you may not be able to come to class some times. You can miss up to 6 classes with no penalty to your grade. These are intended to cover all absences for any reasons, excused or unexcused. For this reason, you do not need to provide any excuses, nor will excused absences be tracked separately.
Please do not come to class if you are ill with a contagious disease, including COVID-19. If you are recently recovering from a respiratory illness and have ended your isolation period, consider wearing a mask out of respect for your classmates while you fully recover.
If you believe that you will need to miss more than 6 classes in total due to circumstances beyond your control, please contact the instructor as soon as possible to discuss accommodation.
Collaboration
The course project will be collaborative: You will research an algorithmic system in a group of your peers. All members of groups are expected to contribute actively to all parts of project work, including meeting, research, development, and writing. No group member should be relegated to only working on a single portion of the project. In addition to specifically individual assessments, individual contributions on group deliverables will be specified and may be taken into account when grading. If you feel unable to successfully participate in collaborative work, you should contact the instructor prior to the add/drop deadline to discuss whether you will be able to complete this course.
Generative AI and Large Language Models (LLMs)
You may choose to use LLMs in this course but should do so according to our guidelines of appropriate use.
Appropriate uses of LLMs (such as ChatGPT) include editing, requesting examples, and asking questions to aid your understanding. You should know that these systems are not perfect, but they may be helpful for summarizing, providing examples, and sifting through lots of information.
Inappropriate uses of LLMs include using such models to draft code or writing and then presenting that as your own, with or without editing. Any code or writing you generate by an LLM should be specifically cited as such, and does not constitute an an original contribution by you. Consider this the same as if you were copying code from an open repository on Github: You need to cite your source. To do otherwise is a violation of academic integrity. Another example of inappropriate use is generating research citations without verifying the integrity of those sources.
Disability Accommodation
If you are a student with a disability and need accommodations for this class, it is your responsibility to register with the Student Disability Access Office (SDAO) and provide them with documentation of your disability. SDAO will work with you to determine what accommodations are appropriate for your situation. Please note that accommodations are not retroactive and disability accommodations cannot be provided until a Faculty Accommodation Letter has been given to me. Please contact SDAO for more information: sdao@duke.edu or access.duke.edu.
