 |
| 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 (6541) | Fr 12:00PM - 12:50PM | Lecture Hall B | BAI, Ge CHEN, Changhao CHEN, Yingcong CHU, Xiaowen DAI, Enyan HU, Zhiming LIU, Li RIKOS, APOSTOLOS SHU, Yao XIE, Zeke YANG, Menglin YUE, Yutao ZHONG, Bingzhuo | 200 | 199 | 1 | 0 |
| DESCRIPTION | This course provides students who lack neural network background with an extensive exposure to modern neural networks. Topics include modern neural network architectures, deep learning theory, training, optimization, regularization, foundation models, deep generative models, transfer learning, and practice experience in the wild. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6996) | Th 06:00PM - 08:50PM | Rm 228, E2 | XIE, Zeke | 80 | 68 | 12 | 0 |