ZHANG, Liang - HKUST Class Schedule & Quota

Class Schedule & Quota
2026-27 Fall 2025-26 Summer 2025-26 Spring 2025-26 Winter
2026-27 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 CNCC CNGF DLED DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS MOES MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

DESCRIPTION	This is the compulsory one-year development course for FTEC students. The course provides academic and professional advising to students on the development of professional ethics, social awareness, responsibilities, and communication skills. An intended learning outcome is to develop a holistic, interdisciplinary, and evidence-based understanding of the issues in FinTech in the financial markets. Grading Type: Pass/ Fail

DESCRIPTION

This is the compulsory one-year development course for FTEC students. The course provides academic and professional advising to students on the development of professional ethics, social awareness, responsibilities, and communication skills. An intended learning outcome is to develop a holistic, interdisciplinary, and evidence-based understanding of the issues in FinTech in the financial markets. Grading Type: Pass/ Fail

FTEC 1010 - Academic Orientation for FTEC Students (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6624)	Tu 04:30PM - 05:20PM	Rm 147, E1	CAI, Ning LI, Siguang QIU, Shi SUN, Shuo WANG, Junxuan WANG, Xuechao YUAN, Zixuan ZHANG, Chao ZHANG, Guang ZHANG, Leifu ZHANG, Liang ZHANG, Yi ZHU, Zimu	60	58	2	0

COURSE INFO

PREVIOUS CODE	FTEC 6910H
DESCRIPTION	Graph data is pervasive in finance and business management. This course introduces essential graph types and foundational modeling methods, including shallow embeddings, graph neural networks, and emerging LLM-based techniques, with emphasis on FinTech applications. Students will apply these approaches to real-world tasks, such as financial fraud detection and recommendation, through reproducible, practice-oriented implementations.

PREVIOUS CODE

FTEC 6910H

DESCRIPTION

Graph data is pervasive in finance and business management. This course introduces essential graph types and foundational modeling methods, including shallow embeddings, graph neural networks, and emerging LLM-based techniques, with emphasis on FinTech applications. Students will apply these approaches to real-world tasks, such as financial fraud detection and recommendation, through reproducible, practice-oriented implementations.

FTEC 5330 - Graph Machine Learning: Methods and Applications (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6534)	We 06:00PM - 08:50PM	Rm 202, E1	ZHANG, Liang	20	11	9	0

[Matching between Lecture & Lab required]

COURSE INFO

DESCRIPTION	This course introduces students to the world of computer science, data analysis, and artificial intelligence. Through a series of lectures and hands-on exercises, students will learn the basics of each of these disciplines, and how they can be used to solve real-world problems. It will cover the following topics: an introduction to computer science, including an overview of its principles and concepts; the basics of data analysis, including methods for collecting, organizing, and analyzing data; an introduction to artificial intelligence, including an exploration of its various applications and capabilities; and an examination of how computer science, data analysis, and artificial intelligence can be used in combination to solve real-world problems. Upon completion of this course, students will have a strong foundation on which to build more advanced knowledge in these exciting fields.

DESCRIPTION

This course introduces students to the world of computer science, data analysis, and artificial intelligence. Through a series of lectures and hands-on exercises, students will learn the basics of each of these disciplines, and how they can be used to solve real-world problems. It will cover the following topics: an introduction to computer science, including an overview of its principles and concepts; the basics of data analysis, including methods for collecting, organizing, and analyzing data; an introduction to artificial intelligence, including an exploration of its various applications and capabilities; and an examination of how computer science, data analysis, and artificial intelligence can be used in combination to solve real-world problems. Upon completion of this course, students will have a strong foundation on which to build more advanced knowledge in these exciting fields.

UFUG 1601 - Introduction to Computer Science (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L04 (6673)	Mo 10:30AM - 01:20PM	Rm 228, E2	ZHANG, Liang	66	53	13	0