Instructor: Yi Wu, Yang Gao
This course aims at providing freshmen students with a broad overview of the Artificial Intelligence field, including computer vision, robotics, reinforcement learning, AI systems, and AI algorithms, motivating them to study the field, and encouraging them to conduct indepth investigation on different areas of the field. It is a required course for freshmen students in the Special Artificial Intelligence Polit Class. Lectures will be given by leading experts in AI areas from both academia and industry.
Instructor: Huanchen Zhang
This course is designed to introduce the fundamental concepts and implementations of modern database management systems. This is not a course that teaches you how to build database applications (e.g., schema design, SQL programming). It is designed as a systems course, with an emphasis on database internals. Topics include relational model and SQL, storage and indexing, query processing and optimization, transactions and concurrency control, distributed and cloud databases, as well as advanced research topics in the field. Students taking this course should have basic knowledge on computer systems. No prior database experience is assumed. The course consists of lectures, written assignments, and projects. Assignments and projects are designed to reinforce what the student learned in lectures and to provide hands- on experience in building a database system. Upon successful completion of this course, the student should feel confident taking a job as a database developer or conducting database-related research in graduate school.
Instructor: Zhilin Yang
This course will introduce important problems in the field of natural language processing such as language modeling, machine translation, and question answering, as well as core technologies to solve these problems including attention-based neural networks and language model pretraining. The course will cover basic algorithms, real-world applications, as well as open problems in academic research.
Instructor: Yang Yuan
This course is a core course in IIIS Zhi Class, which aims for letting students solve interdisciplinary problems using AI techniques, assuming that the students have already taken systematic AI courses. This course contains multiple themes, where each theme contains a few different projects. Students will form teams of size 1-2 people. Each team will pick one project, and solve the specific problems using AI techniques. The goal of this course is to let students finish one AI project from the beginning to the end, understand the potentials and limitations of AI techniques, as well as understand what kind of human/data support are necessary for making AI work. This course assumes that the students have already taken Machine Learning and other related AI course, and also familiar with basic tools (including Python, GitHub, SSH and so on).
Instructor: Luming Duan
This course will organize students to discuss fundamental concepts and big open questions in AI and quantum computer science, including the free will, consciousness, and their relation and implementation with AI and quantum computer, emotion and social intelligence, quantum computing and its relation with brain and AI, creativity and its implementation with AI, how life and intelligence influence each other, and the future directions of intelligence. Through a series of heuristic discussions of fundamental questions, this course will stimulate the students’ interest in research on intelligence and related interdisciplinary subjects and motivate the students to think deeply on unusual fundamental questions to bring conceptual breakthroughs to the filed. Some heuristic questions include: Is free will real or an illusion? What leads to free will (or free-will illusion)? Does machine have free will? What is consciousness/mind? How consciousness helps in intelligence? How to implement consciousness in AI? Does emotion help for intelligence? How to implement emotion and social intelligence in AI? Is consciousness required for social intelligence? Is our brain quantum? How can quantum computing help for intelligence? What are the key factors for creativity? How to make a computer be creative and have imagination? How to implement Inductive, Deductive, Analog, and Probabilistic reasonings with a computer? How life and intelligence mutually influence each other? How to make a computer alive and intelligent? What are the advantages of biologically-based and computer-based intelligence? What are the possible directions for future intelligent beings?
Instructor: Jianyu Chen
This course introduces both the theoretical foundations and advanced techniques in the fields of intelligent systems and robotics, from a unified algorithmic view of both the traditional robotic control perspective and the learning perspective. The contents range from robotic system modeling and problem formulation, planning and control, estimation and perception, to adaptive behaviors using both the indirect (model-based learning) methods and direct (model-free learning) methods. The course concludes with an introduction to industrial robotic arms, autonomous vehicles, and other areas.