 |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | 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 a basic understanding of problem solving, search, theorem proving, knowledge representation, reasoning and planning methods of AI; and develop practical applications in vision, language, and so on. Topics include foundations (search, knowledge representation, machine learning and natural language understanding) and applications (data mining, decision support systems, adaptive web sites, web log analysis). |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6023) | 15-JUN-2026 - 29-JUL-2026 WeFr 03:00PM - 05:50PM | Rm 122, E1 | LIU, Li | 50 | 0 | 50 | 0 | > Add/Drop Deadline: 24 June 2026 |
| PRE-REQUISITE | UFUG 2601 OR UFUG 2602 |
|---|---|
| DESCRIPTION | 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 object-oriented programming. Students then explore machine learning essentials using NumPy, Matplotlib, and scikit-learn for scientific computing, visualization, and implementing basic supervised and unsupervised models. The course further introduces deep learning with PyTorch, covering neural networks, optimization, transformers, large language models (LLMs), and AI agents. A distinctive module focuses on programming with AI, examining AI coding copilots, their workflows, practical strengths and limitations, and how to collaborate effectively with these tools. The course concludes with a final capstone project in which students design, implement, and present a substantial AI application, demonstrating their ability to apply Python both to build AI systems and to leverage AI tools in modern development. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6024) | 15-JUN-2026 - 27-JUL-2026 MoWe 09:00AM - 11:50AM | Rm 202, W4 | CHEN, Huangxun | 40 | 0 | 40 | 0 | > Add/Drop Deadline: 17 June 2026 |
| PRE-REQUISITE | AIAA 2205 AND AIAA 2711 |
|---|---|
| DESCRIPTION | 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 for human visual attention modeling, human body movement modeling, human emotion modeling, human activity and intention recognition, and human cognitive state detection, etc. On the second topic, this course will introduce interactive machine learning, human-centered AI system design, AI assistant and agent, etc. On the third topic, this course will introduce explainable AI, AI ethics, AI fairness, and AI trust. Students are required to accomplish mini course projects to enhance the knowledge and skills that they learn in this course. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6076) | 15-JUN-2026 - 27-JUL-2026 MoWe 03:00PM - 05:50PM | Rm 101, W4 | HU, Zhiming | 40 | 0 | 40 | 0 |
| PRE-REQUISITE | AIAA2205 OR AIAA2211 OR AIAA3201 OR AIAA3225 |
|---|---|
| DESCRIPTION | 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 the pipeline could cause unique security and privacy issues, causing financial and even safety damage to users, model owners, system integrators, etc. Using the framework of security CIA triad and privacy theories, the course will introduce attacks that disrupt or dictate the behavior of AI-based control systems, and those that steal private user and model information. Another emphasis will be on the corresponding protection methodologies and implementations. Course projects will have students build AI systems such as voice assistants from scratch and test various offensive and defensive techniques on them. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6077) | 15-JUN-2026 - 28-JUL-2026 TuTh 01:30PM - 02:50PM | Rm 101, W4 | LONG, Yan | 40 | 0 | 40 | 0 | |
| T01 (6078) | 15-JUN-2026 - 28-JUL-2026 TuTh 03:00PM - 04:20PM | Rm 101, W4 | LONG, Yan | 40 | 0 | 40 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course helps students to get basic knowledge about deep neural networks, helping them to understand basic concepts, capabilities and challenges of deep neural networks. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6067) | 15-JUN-2026 - 27-JUL-2026 MoWe 01:30PM - 04:20PM | Rm 103, E1 | ZHAO, Tianxiang | 20 | 0 | 20 | 0 | > Add/Drop Deadline: 17 June 2026 > Extended Drop Deadline: 24 June 2026 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Learning to make good decisions is one of the keys to autonomous systems. This course will focus on Reinforcement Learning (RL), a currently very active subfield of artificial intelligence, and it will discuss selectively a number of algorithmic topics including Markov Decision Process, Q-Learning, function approximation, exploration and exploitation, policy search, imitation learning, model-based RL and optimal control. This course provides both the foundations and techniques for developing RL and deep RL algorithms that interact with physical environments, and real application cases of RL will be introduced. Basic knowledge of machine learning and mathematical optimization are expected for this course. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6066) | 15-JUN-2026 - 28-JUL-2026 TuTh 06:00PM - 08:50PM | Rm 201, E1 | RIKOS, APOSTOLOS | 40 | 0 | 40 | 0 | > Add/Drop Deadline: 18 June 2026 > Extended Drop Deadline: 25 June 2026 |