This course aims at introducing the fundamentals in algorithms, data and systems of the autonomous intelligent systems, which often refers to the autonomous driving and robotics applications. As the fast advances in the field of AI, how to utilize the learning-based, data-driven approaches to improve the applications for the better human life, becomes very pivotal. We will address the key challenges in this domain, such as (i) how to formulate a system that is equipped with generalization, intelligence and reliability merits. (ii) How to balance the data distribution between simulation and real-world data. (iii) Is scaling law the only pathway towards high-level AGI.

We will introduce the concepts, principles and knowhow to build the autonomous intelligent systems. The basic fundamentals would be detailed in the lectures, with tutorials and hands-on training sessions. All the important topics will be covered, such as imitation learning, reinforcement learning, and so on, with a focus on the applications in autonomous driving and robotics. The highlights in this course would consist of several guest lectures from outside renowned speakers from both industry and academiato address the latest advances in this field. The hands-on session is akin to tutorials or hackathons where students learn the recipe of technologies from scratch quickly. These features would be complementary to the main lecture and facilitate the final group presentation.

The course includes 2 hours of lectures (by the instructor or guest lecture) and 1 hour discussion/tutorial/hands-on session (by a TA) per week. Homework includes both written exercises and programming.