Course Catalog Description

Introduction

This 1-credit course will cover and aims for students to gain a general understanding of Large Language Models (LLMs). There are no course prerequisites, but students will need (1) general comfort using a computer with a command-line application like Terminal or PowerShell and (2) knowledge of Python or a commitment to learning it.

Campus	Fall	Spring	Summer
On Campus	X	X
Web Campus

Instructors

Professor	Email	Office
Ed Loeser	eloeser@stevens.edu	Babbio 109

More Information

Course Outcomes

This 1-credit course will cover and aims for students to gain a general understanding of Large Language Models (LLMs), particularly:

how to take advantage of in-context learning when prompting LLMs
how to use LLMs programmatically
how to use open LLMs
other related topics (tooling, architecture, etc)

Course Resources

Textbook

There is no required textbook. All material will be introduced during class.

Grading

Grading Policies

Attendance - 10%
Homework - 65%
Project - 25%

Lecture Outline

	Topic	Assignment
Week 1	course overview, Python setup	HW 1
Week 2	LLM overview, Python cont’d	HW 2
Week 3	prompting intro, using an API	HW 3
Week 4	prompting (zero-shot, few-shot), LLM APIs	HW 4
Week 5	prompting (chain-of-thought, others), LLM APIs	HW 5
Week 6	practical use cases in Python	HW 6
Week 7	practical use cases in Python	HW 7
Week 8	open LLMs overview, project intro	HW 8, project
Week 9	using open LLMs	HW 9, project
Week 10	using open LLMs, use cases in Python	HW 10, project
Week 11	using open LLMs, use cases in Python	HW 11, project
Week 12	other topics: agents, fine-tuning, RAG	HW 12, project
Week 13	other topics: underlying APIs, learning frameworks	HW 13, project
Week 14	project presentations