| 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 (6417) | Mo 04:30PM - 05:20PM | Rm 101, W1 | BAI, Ge CHEN, Huangxun CHU, Xiaowen KAN, Ge Lin LIANG, Junwei QIN, Chengwei RIKOS, APOSTOLOS WANG, Xin WANG, Zeyu XIE, Sihong XIE, Zeke YANG, Menglin YUE, Yutao | 100 | 55 | 45 | 0 | The class will be delivered by the following instructors and contents as below: W1: Zeke XIE - Overview of Deep Learning Theory W2: Yutao YUE - Towards Conscious AI W3: Menglin YANG - Foundation of LLM: From Pretraining, Mid Training to Post Training W4: Xiaowen CHU - Magic of Low-Precision Computing behind LLMs W5: Sihong XIE - Interpretable AI W6: Xin WANG - Foundations of Quantum Computing W7: Ge BAI - Quantum Entanglement and Applications W8: Chengwei Qin - LLM Reasoning and Agent W9: Zeyu WANG - Toward Synergistic Human-AI Content Creation W10: Huangxun CHEN - Overview of Embedded AI W11: Ge Lin Kan - Generative AI for Urban Digital Twin W12: Junwei Liang - Towards General Service Embodied AI W13: Apostolos RIKOS - AI and Multi-Agent Networks |
| PRE-REQUISITE | DSAA 2043 |
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| DESCRIPTION | The course aims to introduce advanced methods for designing and analyzing algorithms for solving tough problems. The topics cover useful advanced data structures, graph algorithms, heuristic searching algorithms and optimization algorithms that promote the development of AI. Also, students will learn algorithm design and analysis skills for computationally intractable problems, such as NP-completeness, randomized algorithms, approximation algorithms and amortized analysis. Additional topics, such as string matching, geometric and number-theoretic algorithms, will also be introduced. The course presents the topics at an introductory level and aims at senior undergraduate students. Through the class, students will develop a deeper understanding of algorithm design and demonstrate a basic understanding of the principles of advanced algorithms. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6431) | Fr 09:00AM - 11:50AM | Rm 201, W2 | BAI, Ge | 40 | 17 | 23 | 0 |