COURSE DIRECTORY
每张卡片代表一个 Course。打开后选择具体的 Semester 和 Section，再查看详情。
COURSE
This course provides guidance to undergraduate students of the AI major for their academic path and future. This course is mostly introductory and aims to inspire UG students for their academic path development and growt
COURSE
A compulsory course for students enrolling in the Extended Major in Artificial Intelligence throughout their course of study. It is a seminar series presented by faculty members and guest speakers on interdisciplinary to
COURSE
The objective of this course is to present an overview of the principles and practices of AI and to address complex real-world problems. Through introduction of AI tools and techniques, the course helps students develop
COURSE
This course aims to give an introduction to the basic elements of artificial intelligence (AI) through understanding examples from various applications and hands-on experimentation using AI software. In addition to cover
COURSE
This introductory course surveys the explosive area of AI ethics and illuminates relevant AI concepts with no prior background needed. Key topics include Fake News Bots; AI Driven Social Media Displacing Traditional Jour
COURSE
This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative,
COURSE
The implementation of autonomous systems requires agents to learn how to make decisions. Reinforcement learning is a powerful paradigm for achieving such a goal, and it is relevant to an enormous range of tasks, includin
COURSE
Quantum computing represents a revolutionary computing paradigm with transformative potential for next-generation AI. This course provides a comprehensive introduction to quantum computing and quantum AI, guiding student
COURSE
In the era of AI, understanding the profound mathematical bedrock that shapes how we process, compress, and learn from data has become essential. This course, Information-Theoretic Foundations of Compression and Learning
COURSE
This undergraduate course provides a solid foundation in Python programming tailored for artificial intelligence applications. It begins with core Python concepts, including data types, control flow, functions, and objec
COURSE
This course explains the fundamental principles, uses, and some technical details of data mining techniques through lectures and real-world case studies. The emphasis is on understanding the business applications of data
COURSE
This course aims to provide a solid understanding of modern Computer Vision. It starts with essential backgrounds in image processing and classical vision methods, then transitions to contemporary learning-based techniqu
COURSE
Learning and optimization serve as the foundational block for many artificial intelligence algorithms. Our initial focus is on convex analysis and on modeling problems as convex problems, while later on in the course we
COURSE
This course introduces basic concepts, technologies, and applications of Artificial Intelligence of Things (AIoT). It features fundamental knowledge on modern sensor technologies in terms of hardware, software, and appli
COURSE
This course first introduces key concepts and core activities in Software Engineering, including requirements engineering, software development, validation, and maintenance. It then examines recent advances in Artificial
COURSE
This course aims to provide an introduction to the basic elements of operating systems. This course covers topics including basic operating system abstractions, mechanisms and their implementations. The course is divided
COURSE
This course focuses on human-centered AI techniques that include three main topics: AI for understanding human, human-AI interaction, and human-AI coexistence. On the first topic, this course will introduce AI methods fo
COURSE
This course provides a comprehensive journey into Natural Language Processing, first establishing the essential computational fundamentals of words, syntax, and semantics, and then advancing to the modern architectures t
COURSE
The major objective of the course is to teach students the principles of probabilistic models and deep (unsupervised) models in Machine Learning and Artificial Intelligence, and how to use existing tools to implement tho
COURSE
This course introduces the fundamentals of embodied AI. Students will explore key principles and algorithms to build modern autonomous embodied AI systems. Key topics include machine perception, planning and decision-mak
COURSE
This course explores the intersection of AI and physical interaction, focusing on how AI agents learn to execute complex mobility and manipulation tasks. Students will study fundamental algorithms—including reinforcement
COURSE
This course provides an introduction to statistical machine learning algorithms for structured data such as text sequences, taxonomy trees, relational databases (such as knowledge bases), and graphs (including graph data
COURSE
This course introduces trending AI security and privacy problems and provides concrete examples of real-world applications. It dissects the AI/ML pipelines and common ecosystems, and investigates how each component in th
COURSE
This course provides an overview of the main branches of Al and Big Data, and exposes students to real-world industry use cases to help them understand the impact that Al will have on the finance industry. Upon completio