GONG, Zijun - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Fall 2024-25 Summer 2024-25 Spring 2021-22 Winter
2025-26 Fall
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

PRE-REQUISITE	DSAA 2011
DESCRIPTION	Learning and optimization serve as the foundational block for many artificial intelligence algorithms. Our initial focus is on convex analysis and on modeling problems as convex problems, while later on in the course we will shift the focus to different algorithms for convex optimization and nonconvex optimization. The techniques introduced in this course will be motivated by needs of problems and applications in Machine Learning and Deep Learning. The topics range from foundational material to cutting-edge trends.

PRE-REQUISITE

DSAA 2011

DESCRIPTION

Learning and optimization serve as the foundational block for many artificial intelligence algorithms. Our initial focus is on convex analysis and on modeling problems as convex problems, while later on in the course we will shift the focus to different algorithms for convex optimization and nonconvex optimization. The techniques introduced in this course will be motivated by needs of problems and applications in Machine Learning and Deep Learning. The topics range from foundational material to cutting-edge trends.

AIAA 3225 - Learning and Optimization for Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6399)	Th 03:00PM - 05:50PM	Rm 122, E1	GONG, Zijun WANG, Xin	50	27	23	0

COURSE INFO

VECTOR	[3-0-0:3]
EXCLUSION	IOTA 5108
CO-LIST WITH	IOTA 5108
DESCRIPTION	This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.

INTR 5320 - Incremental Learning and Adaptive Signal Processing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6146)	Th 09:00AM - 11:50AM	Rm 228, E2	GONG, Zijun YANG, Liuqing	20	6	14	0

COURSE INFO

VECTOR	[3-0-0:3]
EXCLUSION	INTR 5320
CO-LIST WITH	INTR 5320
DESCRIPTION	This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.

IOTA 5108 - Incremental Learning and Adaptive Signal Processing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6166)	Th 09:00AM - 11:50AM	TBA	GONG, Zijun YANG, Liuqing	20	2	18	0	The classroom is E2228.