# COS 435 / ECE 433: Introduction to Reinforcement Learning
syllabus / schedule / Ed / Gradescope
* Q: How do I join the class/waitlist?
* If you are unable to enroll on the registrar website, then we have likely hit the course cap. Please add yourself to the [waitlist](https://docs.google.com/forms/d/e/1FAIpQLSdkSgQRW2X5hp-aqphmzIP09d2VDiM80XJHtGP8LJo3dN2rAw/viewform)
* Note that 20 seats are reserved for graduate students. I'll start pulling students from the waitlist, and will continue as students drop the course during the start of the semester. My current plan is that the waitlist will be sorted by (1) did you fulfill the prereqs and then (2) seniority (PhD > MS > senior ...) and then (3) randomly. Last year we had 80 students on the waitlist and effectively everyone who waited out the first two weeks was able to enroll. Please do come to the first few lectures.
* Will the course cap be expanded? No
* Can you reallocate the seats reserved for grad students? Yes, after grad student registration.
* Q: Can I audit the course? No.
* Lecture: Tuesday and Thursday, 1:30pm – 2:50pm (room tbd)
* Precepts: Fri 11:00 – 11:50, 12:30 – 1:20 (room and TAs tbd)
* Office Hours (no office hours during Spring Break):
* **Ben Eysenbach**: Tuesday at 3:00pm -- 5:00pm in CS Building 416
* **TAs**: tbd in tbd
* Prerequisites:
* Intro to ML: COS 324, ECE 435 or equivalent
* Probability: ORF 309, or equivalent
* Linear Algebra
* Textbook: None are required, but see [Syllabus](https://docs.google.com/document/d/1zI8TsqTqfQRoEDaaPzeVCcdVwV0UHn6D/edit?usp=sharing&ouid=114713110862674988879&rtpof=true&sd=true) for some books that might be useful if you're ever confused about any of the material in the course.
* Questions? Ask on [Ed](https://edstem.org/us/courses/54890/discussion/). See the instructors during class to get added to Ed.
 _**Reinforcement learning (RL)** is a machine learning technique that teaches agents how to make decisions that lead to good outcomes. This course will introduce fundamental concepts, important RL algorithms, and key challenges (e.g., exploration and generalization). The course will also highlight applications of RL to real-world problems, including health care and molecular science. Assignments will entail implementation of RL algorithms and mathematical analysis of these algorithms. Students will complete an open-ended final group project._
### Assignments
We have provided both the assignment and the TeX file for use as a template. Please type up your solutions using TeX and submit both your compiled PDF and finished .ipynb on Gradescope.
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### Solutions
Solutions will be posted after each assignment is due.
### Lecture Notes
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### Course Staff
](https://ben-eysenbach.github.io/assets/img/prof_pic.jpg height=150)
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