time: Mon/Wed 1:45-3:15pm

location: 3401 Walnut Street, Room 401B

instructors: Osbert Bastani and Rajeev Alur

teaching assistant: Alaia Solko-Breslin

collaboration policy: You are responsible for knowing Penn's Code of Academic Integrity. In particular, copying solutions from other students or other resources (e.g. the web or from students who have taken the class in previous years) is NOT allowed. Making answers to homeworks or exams available to others either directly or by posting on the web is also NOT allowed. We will not have a sense of humor about violations of this policy!

links: We will use Ed Discussion for questions and communication, and GradeScope to submit assignments. We encourage students to use Google Colab for coding assignments.

attendance: We expect students to attend classes regularly. However, please do not come to class if you are not feeling well or test positive for Covid-19. We will do our best to provide lecture slides (on this website) for students unable to make it to class.

description: Recent advances in machine learning---in particular deep neural networks and large language models, are transforming the design and implementation of decision making systems. However, due to their black-box nature, brittleness, and lack of safety guarantees, significant challenges remain in their adoption in critical and potentially high payoff applications such as autonomous systems and healthcare. The vibrant field of "Trustworthy ML" is developing methods and tools to address questions such as: how can we ensure that a decision recommended by an ML system is always safe? how can we explain the decision made by an ML system to a stakeholder? how can we ensure that an ML system makes its decisions in a fair and ethical manner? The goal of this course is to introduce students to state-of-the-art research in trustworthy ML.

prerequisites: The main prerequisite is CIS 5200 (Machine Learning). The course requires both mathematical maturity, including experience with mathematical proofs, and familiarity with machine learning libraries. It is appropriate for students who wish to pursue research in machine learning.

coursework: There will be four homeworks corresponding to the four course modules. The homework problems will be a mix of mathematical problems and programming exercises. There will be no exams. Each student will be required to do a class project and give a presentation about the project at the end of the semester.