# Syllabus

CS 6741 is a graduate course focusing on large language models. As a group we will develop a reading list of papers in this area and work our way through them in depth throughout the semester. The main goal of the course will be to produce a novel research paper in this area by the end of the semester. Students will have the option to work as teams or as individual researchers.

# Structure

The course will consist of two meetings a week where we will go over papers in the area, interspersed with lectures and lab session on difficult topics. The main homework will consist of paper reviews posted in this discourse.

# Grading and Expectations

Grading will have three components.

* Paper reading and class preparation.
* An intermediary replication project, completed as a midterm assignment for the course
* A final project in the form of a research paper at the end of the semester.

# Thematic Focus

We will develop a thematic focus after the bootcamp section of the course. 

# Background 

This course is not meant to be a comprehensive treatment of NLP or deep learning. For NLP, I will assume a working knowledge of basic problems such as classification and language modeling. For deep learning, I will assume knowledge of PyTorch and the ability to quickly pick-up deep learning models from tutorials.

# Course Attendance

Attendance in person is a required component for the course unless permission is given. I will allow a small number of "zoom sessions" per student (and there will be several sessions that I attend remotely), but in general you are required to attend the course. Please contact me if you plan to zoom in.