LU, Zhonghai - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Summer 2025-26 Spring 2025-26 Winter 2025-26 Fall
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

VECTOR	[0-1-0:1]
DESCRIPTION	This course aims at providing research postgraduate students basic training in scientific ethics in research studies in advanced materials, sustainable energy and environment, earth, ocean and atmospheric sciences and microelectronics, research management, professional career development, and related professional skills. Guest speakers from various professional areas will be invited to share their career paths in professional career developments. Students will have chances to connect to talents in various professional areas. Graded PP, P or F.

VECTOR

[0-1-0:1]

DESCRIPTION

This course aims at providing research postgraduate students basic training in scientific ethics in research studies in advanced materials, sustainable energy and environment, earth, ocean and atmospheric sciences and microelectronics, research management, professional career development, and related professional skills. Guest speakers from various professional areas will be invited to share their career paths in professional career developments. Students will have chances to connect to talents in various professional areas. Graded PP, P or F.

FUNH 6770 - Professional Development for Function Hub (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
T01 (6064)	Tu 06:00PM - 06:50PM	Rm 101, W1	CAI, Lanlan FANG, Ting LIU, Zhen LU, Zhonghai XU, Jiang YANG, Qichun	150	74	76	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	Time-series data represent a major category of real-world data collected over time from various sensors or measurement equipment. This course introduces foundational Artificial Intelligence (AI) methods for analyzing time-series data, in particular, about time-series modeling and prediction. We start from investigating the basic properties of time-series data, then discuss a range of popular AI models widely used for time-series modeling and prediction such as Autoregressive Integrated Moving Average (ARIMA) models, Neural Network (NN), Physics-Informed Neural Network (PINN), Hidden Markov Model (HMM) and Kalman Filter (KF) etc. Besides supervised learning, we also discuss un-supervised learning such as clustering algorithms and Self-Organizing Map (SOM) for analyzing time-series data. Broadly this course is a fundamental AI course for all students who intend to master essential theoretical AI methods and practical AI skills needed to develop, assess, and deploy intelligent functionalities in smart electronic and computer systems, Internet-of-Things (IoT), cyber-physical systems (CPS), and any forecasting-relevant applications in finance, economics, data analytics, and other relevant science & engineering fields. Grading Type: Pass or Fail

VECTOR

[3-0-0:3]

DESCRIPTION

Time-series data represent a major category of real-world data collected over time from various sensors or measurement equipment. This course introduces foundational Artificial Intelligence (AI) methods for analyzing time-series data, in particular, about time-series modeling and prediction. We start from investigating the basic properties of time-series data, then discuss a range of popular AI models widely used for time-series modeling and prediction such as Autoregressive Integrated Moving Average (ARIMA) models, Neural Network (NN), Physics-Informed Neural Network (PINN), Hidden Markov Model (HMM) and Kalman Filter (KF) etc. Besides supervised learning, we also discuss un-supervised learning such as clustering algorithms and Self-Organizing Map (SOM) for analyzing time-series data. Broadly this course is a fundamental AI course for all students who intend to master essential theoretical AI methods and practical AI skills needed to develop, assess, and deploy intelligent functionalities in smart electronic and computer systems, Internet-of-Things (IoT), cyber-physical systems (CPS), and any forecasting-relevant applications in finance, economics, data analytics, and other relevant science & engineering fields. Grading Type: Pass or Fail

MICS 6002K - Artificial Intelligence for Time-Series Analysis (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6100)	Mo 01:30PM - 04:20PM	Rm 202, E1	LU, Zhonghai	20	8	12	0