FTEC - HKUST Class Schedule & Quota

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

PRE-REQUISITE	(UFUG 1102 OR UFUG 1105) AND (UFUG 1103 OR UFUG 1106)
CROSS CAMPUS COURSE EQUIVALENCE	MATH 2421
DESCRIPTION	Sample spaces, conditional probability, random variables, independence, discrete and continuous distributions, expectation, correlation, moment generating function, distributions of function of random variables, law of large numbers and limit theorems.

FTEC 2120 - Probability (4 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6625)	Mo 04:30PM - 05:50PM	Rm 228, E2	WANG, Xiaoyu	40	20	20
L01 (6625)	Fr 12:00PM - 01:20PM	Rm 228, E2	WANG, Xiaoyu	40	20	20
T01 (6626)	Th 05:30PM - 06:20PM	Rm 222, W1	WANG, Xiaoyu	40	20	20

COURSE INFO

DESCRIPTION	The course will give students a deeper understanding of the foundations of probability theory, such as probability theory from a measure-theoretic perspective, convergences of distributions and probability measures, and conditional expectations. During the course, important theorems, such as Radon-Nikodym theorem, Fubini theorem, and general central limit theorems, will be investigated.

FTEC 5031 - Advanced Probability Theory (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6362)	We 04:30PM - 07:20PM	Rm 102, W1	WANG, Xiaoyu	40	29	11	0

COURSE INFO

DESCRIPTION	The objective of this course is to provide students with optimization theory and concepts. Main topics cover linear optimization, simplex method, duality theory, convex analysis, and dynamic programming. The emphasis will be on methodology, modelling techniques and mathematical insights.

FTEC 5032 - Optimization Theory (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6363)	Fr 03:00PM - 05:50PM	Rm 201, E1	ZUO, Ruiting	40	26	14	0

COURSE INFO

DESCRIPTION	This course covers the fundamentals of machine learning and artificial intelligence, and their applications in computer vision, image processing, natural language processing, and robotics. The topics include major learning paradigms (supervised learning, unsupervised learning and reinforcement learning), learning models (such as neural networks, Bayesian classification, clustering, kernels, feature extraction), and other problem solving techniques (such as heuristic search, constraint satisfaction solvers and knowledge-based systems) in AI.

DESCRIPTION

This course covers the fundamentals of machine learning and artificial intelligence, and their applications in computer vision, image processing, natural language processing, and robotics. The topics include major learning paradigms (supervised learning, unsupervised learning and reinforcement learning), learning models (such as neural networks, Bayesian classification, clustering, kernels, feature extraction), and other problem solving techniques (such as heuristic search, constraint satisfaction solvers and knowledge-based systems) in AI.

FTEC 5050 - Machine Learning and Artificial Intelligence (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6368)	Fr 01:30PM - 04:20PM	Rm 102, W1	YUAN, Zixuan	40	33	7	0

COURSE INFO

PREVIOUS CODE	FTEC 6910D
DESCRIPTION	This is a graduate level course in stochastic calculus for MPhil/PhD students in Financial Technology and other related fields. This course aims to provide a rigorous mathematical introduction to the tools of stochastic calculus used in derivative pricing and financial modeling. Topics include Brownian motion, stopping times, stochastic integral, Itô’s formula, stochastic differential equations, martingales, Girsanov’s theorem, option pricing, etc.

PREVIOUS CODE

FTEC 6910D

DESCRIPTION

This is a graduate level course in stochastic calculus for MPhil/PhD students in Financial Technology and other related fields. This course aims to provide a rigorous mathematical introduction to the tools of stochastic calculus used in derivative pricing and financial modeling. Topics include Brownian motion, stopping times, stochastic integral, Itô’s formula, stochastic differential equations, martingales, Girsanov’s theorem, option pricing, etc.

FTEC 5061 - Stochastic Calculus for Financial Technology (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6377)	Mo 01:30PM - 04:20PM	Rm 237, E1	ZHANG, Ying	30	30	0	0

COURSE INFO

DESCRIPTION	This is a course in graduate level microeconomic theory for PhD students in financial technology and other related fields. This course covers topics including consumer theory, producer theory, uncertainty, general equilibrium,and matching. The required background knowledge for the course are intermediate microeconomic theory and mathematics through calculus of several variables and introductory real analysis. Additional mathematical tools will be explained briefly as the course proceeds. This course serves as the first rigorous training in economics and finance and helps lay down a solid foundation in economic modelling for future research.

DESCRIPTION

This is a course in graduate level microeconomic theory for PhD students in financial technology and other related fields. This course covers topics including consumer theory, producer theory, uncertainty, general equilibrium,and matching. The required background knowledge for the course are intermediate microeconomic theory and mathematics through calculus of several variables and introductory real analysis. Additional mathematical tools will be explained briefly as the course proceeds. This course serves as the first rigorous training in economics and finance and helps lay down a solid foundation in economic modelling for future research.

FTEC 5101 - Microeconomic Theory (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6378)	Tu 01:30PM - 04:20PM	Rm 202, E3	LI, Siguang	40	27	13	0

COURSE INFO

DESCRIPTION	This course covers basic pricing theory of financial derivatives and risk hedging of exotic options. The course starts with the fundamental theorem of asset pricing and risk neutral valuation principle. The renowned Black-Scholes pricing theory and martingale pricing theory are introduced. Advanced topics include exchange options, quanto options, implied volatility and VIX.

FTEC 5210 - Quantitative Models for Financial Derivatives (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6537)	Tu 01:30PM - 02:50PM	Rm 147, E1	HAN, Bingyan	35	35	0	0
L01 (6537)	Th 01:30PM - 02:50PM	Rm 102, E1	HAN, Bingyan	35	35	0	0

COURSE INFO

PREVIOUS CODE	FTEC 6910B
DESCRIPTION	This course shows the use of various quantitative techniques and financial engineering principles in the management and modeling of financial risks. The topics include hedging of market risks, immunization of bond risks, Value-at-Risk and expected shortfall, credit yield curve model, credit derivatives pricing, and default correlation models.

FTEC 5230 - Financial Risk Management (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6379)	TuTh 09:00AM - 10:20AM	Rm 228, E2	ZHU, Zimu	40	18	22	0

COURSE INFO

PREVIOUS CODE	FTEC 6910F
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.

PREVIOUS CODE

FTEC 6910F

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 5250 - Machine Learning for Finance (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6532)	We 09:00AM - 11:50AM	Rm 201, W4	ZHANG, Chao	20	20	0	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

COURSE INFO

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 6101H - FinTech Program Seminar (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6894)	Fr 10:30AM - 11:50AM	Rm 122, E1	ZHU, Zimu	70	53	17	0

COURSE INFO

DESCRIPTION	Independent study in a designated subject under direct guidance of a faculty member to provide students the advanced knowledge and research skill sets on a topic of Financial Technology. Required readings, tutorial discussions, and submission of report(s) will be used for assessment. The course may be repeated for credit if different topics are studied. Graded P or F.

FTEC 6900D - Independent Study (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6895)	TBA	No room required	TBA	5	0	5	0

COURSE INFO

DESCRIPTION	This course tackles the challenge of distilling valuable insights from the massive data generated by Internet's expansion. Topics include basics of data warehousing, data cleansing and integration, classification, clustering, association analysis, and anomaly detection. With the industry's surging demand for proficient data analytics, this course equips students with a robust interdisciplinary toolkit to navigate the complexities of big data in FinTech domain.

DESCRIPTION

This course tackles the challenge of distilling valuable insights from the massive data generated by Internet's expansion. Topics include basics of data warehousing, data cleansing and integration, classification, clustering, association analysis, and anomaly detection. With the industry's surging demand for proficient data analytics, this course equips students with a robust interdisciplinary toolkit to navigate the complexities of big data in FinTech domain.

FTEC 6910J - Financial Data Mining and Data Warehousing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6735)	We 01:30PM - 04:20PM	Rm 202, E4	YUAN, Zixuan	20	10	10	0

COURSE INFO

DESCRIPTION	Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

FTEC 6990 - MPhil Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6380)	TBA	No room required	TBA	999	11	988	0

COURSE INFO

DESCRIPTION	Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

FTEC 7990 - Doctoral Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6381)	TBA	No room required	TBA	999	19	980	0