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 |
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L01 (6249) | Fr 09:00AM - 09:50AM | Rm 102, E4 | CHEN, Jintai CHEN, Yingcong DAI, Enyan HU, Xuming LIANG, Junwei LIU, Li RIKOS, APOSTOLOS SUN, Ying WANG, Hao WANG, Xin XIE, Sihong XIE, Zeke YUE, Yutao | 100 | 70 | 30 | 0 | The class will be delivered by the following instructors as below. Week 1: Junwei LIANG Week 2: Xin WANG Week 3: HAO WANG Week 4: Sihong XIE Week 5: Li LIU Week 6: Zeke XIE Week 7: Yutao YUE Week 8: Xuming HU Week 9: Jintai CHEN Week 10: Yingcong CHEN Week 11: Ying SUN Week 12: Enyan Dai Week 13: Apostolos Rikos |
VECTOR | [3-0-0:3] |
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DESCRIPTION | Graph mining methods have been investigated for various applications including financial analysis, traffica prediction, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that existing graph mining methods can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data, and lack interpretability. In this course, representative graph mining models and their inner mechanisms will be discussed. Then, we will introduce the trustworthy graph mining methods in privacy, robustness, fairness, and explainability. |
Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
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L01 (6010) | TuTh 10:30AM - 11:50AM | Rm 233, W1 | DAI, Enyan | 30 | 6 | 24 | 0 |