CHEN, Huangxun - 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
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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	0	100	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance.

VECTOR

[3-0-0:3]

DESCRIPTION

This course explores cutting-edge techniques for creating efficient machine-learning models to address the growing demand for real-time decision-making and localized processing across diverse application fields, including IoT/robotics/smart manufacturing systems and beyond. Key topics include model compression, pruning, quantization, neural architecture search, knowledge distillation, on-device fine-tuning, transfer learning, application-specific acceleration techniques, etc. Through hands-on projects, students will learn to optimize and adapt deep learning models for resource-constrained devices while maintaining accuracy and performance.

IOTA 5202 - Efficient Machine Learning for Resource Constrained Environments (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6108)	Tu 01:30PM - 04:20PM	Rm 102, W1	CHEN, Huangxun	30	0	30	0

COURSE INFO

VECTOR	[0-1-0:1]
DESCRIPTION	A series of regular seminars presented by postgraduate students, faculty, and guest speakers on IoT-related research problems currently under investigation. Students are expected to attend regularly. Continuation of IOTA 6101. Graded P or F.

IOTA 6102 - Internet of Things Seminar II (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6338)	Tu 04:30PM - 05:50PM	Rm 228, E2	CHEN, Huangxun	50	0	50	0