Large Language Models (LLMs) and their applications have received tremendous attention since the release of ChatGPT in late 2022. Methods for leveraging the power of these models and managing associated risks are continuing to improve, as the models themselves also advance. Recognizing the importances of this fast-moving area, this project-based course provides students an opportunity to learn and apply state-of-the-art methods to make the application of LLMs more trustworthy, with attention to topics such as relevance, groundedness, confidence, calibration, uncertainty, explainability, privacy, fairness, toxicity, and adversarial attacks.
Students complete two homework assignments to acquire important background, with most of the course devoted to the quarter long course project. The course has three main focus areas:
Introductory Python-based ML class (equivalent to CS229), knowledge of deep learning (such as CS230, CS231N, etc.), and familiarity with ML frameworks in Python like PyTorch, Keras, or TensorFlow.
Permissive but strict. If unsure, please ask the course staff!
We are generally open to in-person auditing requests by Stanford affiliates. Audits can attend lectures if there are available seats after enrolled students have taken their seats. Please come to the lecture, and if there is space available, audits may stay for the session.
Audits are welcome to participate in the discussion in lectures, but we encourage that audits check out the slides from previous lectures first — lectures could be confusing otherwise! Audits are also welcome to attend OHs, though if there are more enrolled students waiting in the line, they’ll be given priority.
We are unable to support remote audits as lectures are held in-person, and are not recorded. The general public can follow along with the course by accessing the published lecture slides, notes, assignments, and final report instructions online. These materials are available on the Syllabus page.
Please note that no course support will be available for auditors. However, we encourage you to share any feedback on the course — if there’s any concept that you find confusing, incorrect or missing, please let us know!
There's no textbook. The course relies on lecture slides and accompanying readings.
The quarter long course project has five major deliverables:
