CHEN, Huangxun - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Fall 2023-24 Summer 2023-24 Spring 2021-22 Winter
2024-25 Fall
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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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 DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

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.

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.

INFH 5000 - Information Science and Technology: Essentials and Trends (2 units)

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

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 (6102)	Mo 01:30PM - 04:20PM	Rm 223, W1	CHEN, Huangxun	30	19	11	0