Overview

Goal

1. Students will be able to critically read, assess, and discuss research work in Trustworthy Machine Learning (TML).
2. Students will gain the technical background to implement basic TML techniques in a deep learning framework.
3. Students will be ready to conduct their own research in TML and make contributions to the research community.

Prerequisites

• Familiarity with Python and PyTorch coding.
• A pass grade from the Deep Learning Course (or equivalent).
• Basic knowledge of machine learning concepts.
• Basic maths: multivariate calculus, linear algebra, probability, statistics, and optimisation.

TML Book

Last year’s course materials are now a book. You can find it here: https://trustworthyml.io/. It’s also on arXiv: https://arxiv.org/abs/2310.08215.

Special thanks to Bálint for the majority of the work. Also, kudos to last year’s tutors: Alex, Elisa, and Michael.

Note: The course materials are updated yearly to stay aligned with the latest research. The book will be useful for the course. However, it won’t cover new topics added to the course.

Policies

Grading

• Participation: 20%
• Assignments: 40%
• Final presentation: 40%

Use of language models

Students may use language models. However, you are solely responsible for all outputs you submit. We will apply heavy penalties for:

• Hallucinated or factually incorrect outputs.
• Unsound or fabricated citations.
• Plagiarised materials.
• AI slop (low-effort, generic AI-generated content).

Severe cases may be reported to the university for disciplinary action.

You must be ready to answer clarification requests from the lecturer or tutors at any point. Inability to explain your own work will be treated as evidence of academic misconduct.

We do not tolerate very similar creative work among class members. AI tends to produce similar outputs across sessions and model families. Diversify your answers, especially for creative work. Suspicion of copied work will be penalised.

Communication

Lecturer

• Seong Joon Oh

Tutors

• Bryan Truong
• Seokwon Jung

Central email

Please use the STAI group email stai.there@gmail.com to

• Submit your exercises;
• Ask questions; and
• Send us feedback.

Slack forum

For those who’re registered for the course, ask the lecturer or tutors to add you to the Slack channel. We need your name and email address. Use it for

• Asking questions;
• Sending us feedback;
• Receiving official announcements; and
• Communicating others (e.g. finding exercise group members).

When & where

Lecture: 양재산학캠퍼스 대강의실 (or Zoom)

• Fridays 13:00-16:00

Schedule & exercises