ZHANG, Chao - HKUST Class Schedule & Quota

Class Schedule & Quota
2024-25 Summer 2024-25 Spring 2024-25 Fall 2021-22 Winter
2024-25 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 DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

VECTOR	[0-1-0:0]
DESCRIPTION	Advanced seminar series presented by guest speakers and faculty members on selected topics in Financial Technology. This course is offered every regular term. Graded P or F.

FTEC 6101E - FinTech Program Seminar (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6368)	Fr 09:00AM - 10:20AM	Rm 101, W1	ZHANG, Chao	70	50	20	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course offers an in-depth introduction to the fundamental concepts of artificial intelligence (AI) and its advanced applications in finance. It covers key AI techniques, including linear models, tree-based methods, clustering algorithms, neural networks, and reinforcement learning, with an emphasis on both theoretical and practical implementation. Applications in finance, including risk management, asset pricing, fraud detection, and high-frequency trading, will be critically analyzed through real-world datasets. The course fosters a deep understanding of AI methods, enabling students to engage in independent research, and contribute to the development of novel financial technologies.

VECTOR

[3-0-0:3]

DESCRIPTION

This course offers an in-depth introduction to the fundamental concepts of artificial intelligence (AI) and its advanced applications in finance. It covers key AI techniques, including linear models, tree-based methods, clustering algorithms, neural networks, and reinforcement learning, with an emphasis on both theoretical and practical implementation. Applications in finance, including risk management, asset pricing, fraud detection, and high-frequency trading, will be critically analyzed through real-world datasets. The course fosters a deep understanding of AI methods, enabling students to engage in independent research, and contribute to the development of novel financial technologies.

FTEC 6910F - Machine Learning for Finance (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6317)	Tu 09:00AM - 11:50AM	Rm 101, W2	XIAN, Chulin ZHANG, Chao	20	10	10	0