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| 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. |
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| 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 | 0 | 200 | 0 |
| PRE-REQUISITE | DSAA2011 AND UFUG2104 |
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| DESCRIPTION | This course is an advanced course in machine learning. It dives into statistical methods that are very popular in modern machine learning systems. It includes topics like supervised learning, dimensional reduction, support vector machines, boosting, sparse methods and deep learning, etc. In this course, we will delve into statistical methods for machine learning. It covers hot topics in statistical learning, aiming to provide students with state-of-the-art statistical tools to enhance modern engineering and science practice. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6564) | Th 12:00PM - 02:50PM | Rm 147, E1 | SHU, Yao | 60 | 0 | 60 | 0 |