ZHANG, Liang - 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.
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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 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 (6409)	We 05:00PM - 05:50PM	Rm 101, E1	BAI, Ruoqi CAI, Ning CAO, Yiyue 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	56	4	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	Graph data are pervasive in finance and business management. The course will be organized in three parts. Part I introduces graph data types and representations, including typical simple/weighted and directed/undirected graphs; bipartite, heterogeneous and knowledge graphs; temporal/dynamic graphs; hypergraphs together with node/edge attributes and common storage/processing patterns. Part II introduces graph data modeling methods from shallow embeddings to modern graph neural networks (message passing mechanism, heterogeneous/temporal architectures, contrastive pretraining) and LLM-based approaches (graph-aware prompting, graph-based RAG, graph foundation models). Part III examines graph data based applications in finance and business management, including anomaly and fraud detection, stock market prediction, and recommendation systems, with emphasis on reproducible, practice-oriented implementation.

VECTOR

[3-0-0:3]

DESCRIPTION

Graph data are pervasive in finance and business management. The course will be organized in three parts. Part I introduces graph data types and representations, including typical simple/weighted and directed/undirected graphs; bipartite, heterogeneous and knowledge graphs; temporal/dynamic graphs; hypergraphs together with node/edge attributes and common storage/processing patterns. Part II introduces graph data modeling methods from shallow embeddings to modern graph neural networks (message passing mechanism, heterogeneous/temporal architectures, contrastive pretraining) and LLM-based approaches (graph-aware prompting, graph-based RAG, graph foundation models). Part III examines graph data based applications in finance and business management, including anomaly and fraud detection, stock market prediction, and recommendation systems, with emphasis on reproducible, practice-oriented implementation.

FTEC 6910H - Graph Machine Learning: Methods and Applications (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6087)	Tu 06:00PM - 08:50PM	Rm 201, E4	HUA, Fengrui ZHANG, Liang	20	10	10	0