EE 260E: Trustworthy AI
Winter 2025
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Course Overview
This course aims to provide you with a comprehensive understanding of the principles and practices that underpin the development of safe and reliable AI and its application to autonomous systems. The course objectives include exploring the foundational and advanced concepts of trustworthy artificial intelligence and machine learning, with a particular focus on their applications in autonomous systems such as autonomous vehicles, manipulators, drones, and other robotic systems. Students will learn to assess and ensure the safety, reliability, and ethical implications of AI algorithms in these contexts. The course will also delve into the latest research and methodologies for creating AI systems that are safe, robust, generalizable, and explainable, emphasizing the importance of trust and ethical considerations in AI deployment.
Through a combination of theoretical knowledge and practical case studies, you will be equipped to design, analyze, and implement AI solutions that are not only technically sound but also socially responsible and trustworthy. There will be a final course project for students to obtain hands-on research experience in related areas.
Note: If you are interested in enrolling in this course but cannot enroll directly, please feel free to reach out to the instructor Prof. Jiachen Li (jiachen.li@ucr.edu) to request approval.
Topics Covered
Topics Covered
Trustworthy AI Principles
Deep Learning & Deep Reinforcement Learning
Out-of-distribution (OOD) Generalization
Adversarial Robustness & Security
Explainability and Interpretability
Uncertainty Estimation/Quantification
Safe Reinforcement Learning & Control
Human-Robot Interaction & Collaboration
Multi-Robot Systems
Foundation Models for Robotics and Autonomy
Course Details
Lectures
Lectures
Wednesdays & Fridays: 3:30 PM - 4:50 PM Dates: 01/08 - 03/14 Classroom: WCH 142
Office Hours
Office Hours
Instructor (Prof. Jiachen Li): Tuesdays: 2:30-3:20 PM Location: WCH 343 Email: jiachen.li@ucr.edu
Teaching Assistant (Jianpeng Yao): Thursdays: 2:00-3:00 PM Location: WCH 315 Email: jyao073@ucr.edu
Prerequisites
Prerequisites
Familiarity with Python and PyTorch coding.
Basic maths: multivariate calculus, linear algebra, probability, statistics, and optimization. These mathematical concepts are fundamental to understanding and implementing AI and ML algorithms.
Basic knowledge of machine learning and deep learning concepts and techniques.
You should be able to read conference or journal papers on robotics, deep learning, and reinforcement learning and have a decent understanding of the basic ideas and concepts proposed (not necessarily a complete understanding of every detail).
Grading Policy
Paper Presentation (20%): Evaluates understanding of research papers and the presentation of key contributions and methodologies.
Paper Summary (20%): You will submit two paper summaries. Note that these two papers should be different from the one you present during the lecture.
Final Project (40%): Assesses research capabilities, requiring strong coding skills and analytical efforts.
Allocation: project proposal (5%); final report (25%); final presentation (10%).
Midterm Exam (15%): Assesses understanding of concepts and knowledge covered before midterm. The exam will tentatively happen on 2/26/2025 during the lecture.
Class Attendance (5%): Lecture attendance is required since it is one of the most important components of the course. Asking questions after paper presentations or final project presentations will receive a bonus (up to 2%).
References
There is no required textbook for this course. An in-depth understanding of the lecture slides and the set of selected papers will be sufficient for you to succeed in this course. Note there is no existing textbook that covers such a collection of topics and materials covered in this course.
Additional Recommended References:
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