BAI, Ge - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Spring 2025-26 Winter 2025-26 Fall 2024-25 Summer
2025-26 Spring
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
» Student Information System (SIS)» PG Program/Course Catalog
Notes & Links
This service was first adapted from a student contribution in 2011-12 Fall. The efforts of all concerned are duly acknowledged.

AIAA AMAT BSBE CMAA CNCC CNGF DLED DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

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.

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.

AIAA 1010 - Academic Orientation for AI Students (1 unit)

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

COURSE INFO

PRE-REQUISITE	DSAA 2043
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.

PRE-REQUISITE

DSAA 2043

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.

AIAA 4711 - Introduction to Advanced Algorithmic Techniques (3 units)

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