Syllabus
Table of Contents
Textbooks
There is no need buy any textbook for this course. We will provide slides or lecture notes in this course. The following are companion textbooks that can provide useful further reading:
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction
- Christopher Bishop and Hugh Bishop, Deep Learning: Foundations and Concepts
Attendance
Class attendance and participation are key for both your and your peer’s success in this class. You are expected to attend class in person during the scheduled time, including the final presentations. I understand that occasionally you may have challenges attending (e.g., illness, religious observance, etc.). However, if you anticipate having a challenge regularly attending class, please contact us.
Holiday Policy
This course will follow the University Policy on Secular & Religious Holidays which can be found in the PennBook. No exams or assigned work will be required on those holidays listed in sections 1 & 2 of the policy. Students who plan to observe any other holiday should contact the instructor within the first two weeks of class so that alternative arrangements can be made.
Academic Integrity
Honesty and transparency are important features of good scholarship. On the flip side, plagiarism and cheating are serious academic offenses with serious consequences. If you are discovered engaging in either behavior in this course, you will earn a failing grade on the assignment in question, and further disciplinary action may be taken. We encourage you to work together on projects (teams up to 2) to make use of campus resources like Penn GSE Student Success to assist you in your pursuit of academic excellence. However, please note that in accord with the university’s policy you must acknowledge any collaboration or assistance that you receive on work that is to be graded, either from a person, reference, or a tool (including AI-generation tools like ChatGPT).
Late Policy
The only time when students have to submit something for this class is for the drone racing competition. The event will be live, so not submitting runnable code on time will be equivalent to a fail grade for the entire team. Not coming to the midterm or final exam will result to a fail grade. No exceptions will be granted.
Communication
- Website: We will use the class website for posting course content (e.g., slides).
- Canvas: We will use Canvas for uploading all assignments.
- Email: If you email your instructors, please include the substring “[RWRL Course]” to begin a meaningful subject line and have tried to resolve the issue appropriately otherwise. Please use your UPenn email account.
Grading
Grading details will be provided in the slides from the first class. Overall, your final grade will be based on a combination of exams and your performance in the drone race.
Health & Wellness
Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.
All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 215-746-WELL (9355) and visit their website at https://wellness.upenn.edu/student-health-and-counseling. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.
If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
CaPS: 215-746-WELL (9355)