 |
| 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 |
| 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 |