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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 (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 | In the era of big data and artificial intelligence, information theory has become an indispensable tool for machine learning practitioners. This course aims to bridge the gap between classical information theory and its cutting-edge applications in machine learning. Students will explore the foundations of information measures, data compression, hypothesis testing, channel coding, channel capacity, entropies, and divergences, as well as their statistical learning applications. Through guest lectures by leading experts, we will also delve into the frontiers of information theory in machine learning. By the end of this course, students will be equipped with the knowledge and skills necessary to apply information theory to develop more efficient and effective machine learning technologies. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6093) | Tu 01:30PM - 04:20PM | Rm 201, E1 | WANG, Xin | 30 | 27 | 3 | 0 |
| VECTOR | [2-0-0:2] |
|---|---|
| DESCRIPTION | This inquiry-based course aims to introduce students to the concepts and skills needed to drive digital transformation in the information age. Students will learn to conduct research, explore real-world applications, and discuss grand challenges in the four thrust areas of the Information hub, namely Artificial Intelligence, Data Science and Analytics, Internet of Things, and Computational Media and Arts. The course incorporates various teaching and learning formats including lectures, seminars, online courses, group discussions, and a term project. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6053) | We 09:00AM - 10:50AM | Lecture Hall A | CHEN, Huangxun TANG, Nan WANG, Xin YU, Luwen | 200 | 199 | 1 | 0 | |
| L02 (6054) | We 02:00PM - 03:50PM | Lecture Hall A | CHEN, Huangxun TANG, Nan WANG, Xin YU, Luwen | 200 | 198 | 2 | 0 |