LAI, Zhilu - 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

PRE-REQUISITE	(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND (UFUG 2106 OR DSAA 2088)
DESCRIPTION	Advances in machine learning and deep machine learning are continuously penetrating computational science and engineering. This course will introduce students to recent advances in data-driven scientific research based on deep learning techniques, enabling next-generation scientists and engineers to leverage AI in crossdisciplinary fields. Topics include but not limited to advanced deep neural network models, physicsinformed neural networks (PINN), model discovery and prediction, uncertainty quantification, and neural operators. Applications to diverse scientific and engineering computational tasks aided by deep learning methods will also be introduced.

PRE-REQUISITE

(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND (UFUG 2106 OR DSAA 2088)

DESCRIPTION

Advances in machine learning and deep machine learning are continuously penetrating computational science and engineering. This course will introduce students to recent advances in data-driven scientific research based on deep learning techniques, enabling next-generation scientists and engineers to leverage AI in crossdisciplinary fields. Topics include but not limited to advanced deep neural network models, physicsinformed neural networks (PINN), model discovery and prediction, uncertainty quantification, and neural operators. Applications to diverse scientific and engineering computational tasks aided by deep learning methods will also be introduced.

DSAA 3074 - Deep Learning for Science and Engineering (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6618)	Th 12:00PM - 02:50PM	Rm 222, W1	LAI, Zhilu	40	13	27	0
T01 (6619)	Fr 10:30AM - 11:20AM	Rm 222, W1	LAI, Zhilu	40	13	27	0

COURSE INFO

DESCRIPTION	Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.

DESCRIPTION

Data-driven modeling is revolutionizing the modeling and predicting of complex systems. This cross-disciplinary course will introduce methodologies for integrating time-series analysis, machine learning, engineering mathematics, and mathematical physics, into data-driven methods for inferring and building models from data. At the end of the course, students are expected to understand the principles and methods of extracting patterns and models from data and making effective predictions, and to have hands-on implementations with Python/Matlab. In-class lab demonstrations will also be provided.

IOTA 5002 - Data-driven Modeling: Learning from Sensor Data (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6382)	Tu 09:00AM - 11:50AM	Rm 102, E1	LAI, Zhilu	30	10	20	0