Policies

Communication Policy

This is a large course, and there are several ways to get in touch with the course staff depending on your need.

• For basic syllabus information, policies, schedule, etc., please see the course website.
• For resources such as lecture notes, recordings, readings, etc., please see Canvas.
• For questions relating to your regular recitation section, please ask your teaching assistants facilitating your recitation section.
• For regular questions about the course related to homework, review, or logistics, please attend helper hours or use Ed Discussion (see the Help page for more details).
• For personal or sensitive questions related to the course (for example, overall course grades or student disability accommodations) you should email the teaching associate and/or instructor. Please do not use email for anything that can be handled through the other means above.

Grades and Exams

Course grades will be calculated by averaging with the following weights.

In addition to the 30% for homework submissions, note that some homework or recitation problems or slight variations will appear on the exams. Seriously attempting to understand the homework and recitation will therefore help you to prepare for exams.

All exams (Midterm, Lateterm, and Final) will be written and in-person. They are closed book and closed note. You may not use any electronic devices of any kind, nor may you communicate with other students in any way about the exams. The Midterm and Lateterm exams will be 75 minutes in duration; the final exam will be 90 minutes in duration.

For the In-Semester exams category of the grade, we will take the better (equivalently, drop the lower) of your Midterm and Lateterm exam score. This also means that the other in-semester exam serves as the makeup exam if you miss one of the Midterm or the Lateterm. You must contact the instructors as soon as possible if you will have excused absences for both of the in-semester exams.

The following schema provides guarantees for letter grades: We guarantee that letter grades will be no stricter than this (that is, the thresholds will not increase). We reserve the right (but do not guarantee) to assign letter grades more generously (that is, to lower these thresholds).

Collaboration & Internet Use on Homework

Collaboration is an encouraged part of the course, but only within boundaries that will ensure your learning and maintain academic integrity. In particular, while you may discuss ideas and study with other students, it is crucial that you come to your own independent understanding and are able to explain that understanding in writing for all assigned homework problems. Just reading or editing the solutions of others (from a current student, someone outside the class, the internet, etc.) will not suffice for learning the material or adequately preparing you for exams or your future in computer science.

You may work with a partner on homework assignments (that is, a group of 2 total), in which case you should each try to write a solution to every problem before discussing and combining your ideas to a final document to submit as a pair. You should never split the problems on an assignment, but should each try every problem. You may also discuss ideas and study with other students beyond a partner in small groups of up to four, but you may not share or compare any written solutions beyond your single partner. You are not required to work with a partner, and we will not assign partners.

You are welcome to use the internet as a resource for understanding general definitions, viewing examples, etc., but may not search the internet for solutions to homework problems. To reiterate: Just reading or editing the solutions of others will not suffice for learning the material.

Generative AI and Large Language Models

You may choose to use LLMs such as ChatGPT in this course but should do so according to the following guidelines of appropriate use. These guidelines are inspired by those of the NeurIPS author instructions, a leading venue for the publication of machine learning research, adapted to an educational context.

• You are ultimately responsible for the entire content of any submitted work, meaning it is your responsibility to ensure the integrity, correctness, and originality of your work.
• You should never characterize work from an LLM as your own without attribution or discussion of the method of your usage.
• In an educational context where the goal is to learn, you should use LLMs to supplement and enhance your learning rather than to substitute for your learning. In particular, appropriate uses might include asking for additional clarification, examples, editing, or critique of your work. Inappropriate uses include using an LLM to solve a problem for you, with or without your subsequent editing, or using text generated by an LLM as your own without attribution, again with or without subsequent editing.

Attendance

Attendance at lecture is generally expected, but absence will not be penalized in your grade. If you miss class, you are still responsible for the material and should review the recording, notes, and associated reference(s).

Attendance and active participation in your recitation section is expected and required. Attendance will be taken. You can miss up to 4 recitation meetings with no penalty to accommodate any illness,travel, etc., No additional allowances will be made. Recitation is the primary opportunity for you to practice in a smaller classroom environment, including time to think, solve problems, discuss with peers, ask questions, and review. Recitation will help you be better prepared for exams. Most of the time, you must attend the recitation section to which you are assigned. However, if you have one or two times during the semester when you have a conflict, you must inform both sets of TAs in advance and they will be able to transfer your attendance credit.