BIOF 495 - Foundation Models and Large Language Models

Course Description

.This course provides a comprehensive introduction to modern foundation models, with a focus on both encoder-based and decoder-based architectures used in today’s natural language processing applications. Students will explore the core components and design principles behind large language models and gain hands-on experience applying models such as GPT for prompting, in-context learning, and instruction-tuned workflows. The course also covers the use of encoder models like BERT for downstream tasks, highlighting their strengths and differences relative to decoder-based systems. Throughout the course, students will learn practical evaluation techniques and develop an understanding of the considerations involved in selecting, deploying, and assessing large language models.

Learner Outcomes

When you complete the course successfully, you will be able to:

Understand the fundamentals of foundation models, including their key components and architectures
Apply decoder-based language models, such as GPT, using prompting, in-context learning, and instruction-tuned methods to solve practical NLP tasks
Apply encoder-based language models, such as BERT, to downstream NLP tasks and understand their strengths relative to decoder-based models
Evaluate large language models and understand the practical considerations involved in their use

Microcredential(s)

This course applies towards the Bioinformatics Endeavor digital badge.

Textbook Information

There is no textbook for purchase required for this course.

Prerequisites

Students should have completed BIOF 395 and have experience with Python. Alternatively, students without this background are expected to familiarize themselves with these skills quickly. Week 1 is designed as a refresher.

Refund
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