 |
| DESCRIPTION | 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 growth of maturity during their UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Graded P or F. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6277) | Fr 03:00PM - 03:50PM | Rm 101, W1 | CHEN, Lei CHEN, Yingcong DAI, Enyan HU, Xuming LIANG, Junwei LIU, Li RIKOS, APOSTOLOS SUN, Ying WANG, Hao WANG, Xin XIE, Sihong XIONG, Hui YUE, Yutao ZHONG, Bingzhuo | 136 | 85 | 51 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Artificial Intelligence technologies have been maturing and are deployed in real-world applications, such as healthcare, entertainment, business, scientific research, military, etc. In all these domains, the decisions made by AI algorithms can critically impact individuals, organizations and society. The designers, auditors, and users of AI technologies thus need to be equipped with the capabilities to understand, analyze, and eventually discipline these algorithms in the broader contexts. This course will introduce students to the latest research of responsible AI and explore these capabilities in both theoretical and practical ways. Topics include but are not limited to theories and algorithms of secure machine learning, fair machine learning, interpretable AI, and case studies involving natural language processing, computer vision, and reinforcement learning. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6241) | Fr 09:00AM - 11:50AM | Rm 102, E1 | XIE, Sihong | 30 | 11 | 19 | 0 |