LU, Zhonghai - 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	[2-0-0:2]
DESCRIPTION	This course covers background knowledge in the thrust areas of the Function Hub, including Advanced Materials, Sustainable Energy and Environment, Microelectronics, and Earth, Ocean and Atmospheric Sciences.

FUNH 5000 - Introduction to Function Hub for Sustainable Future (2 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6066)	Mo 09:00AM - 10:50AM	Rm 101, W1	CHI, Jinshu GAN, Zecheng HUANG, Jiayi LU, Zhonghai YANG, Kezhou YANG, Qichun YU, Liuqian ZHU, Guoyi	150	83	67
L02 (6069)	Tu 09:00AM - 10:50AM	Lecture Hall B	CHI, Jinshu GAN, Zecheng HUANG, Jiayi LU, Zhonghai YANG, Kezhou YANG, Qichun YU, Liuqian ZHU, Guoyi	150	139	11
L03 (6072)	Th 09:00AM - 10:50AM	Rm 101, W1	CHI, Jinshu GAN, Zecheng HUANG, Jiayi LU, Zhonghai YANG, Kezhou YANG, Qichun YU, Liuqian ZHU, Guoyi	150	81	69
L04 (6076)	Fr 09:00AM - 10:50AM	Lecture Hall C	CHI, Jinshu GAN, Zecheng HUANG, Jiayi LU, Zhonghai YANG, Kezhou YANG, Qichun YU, Liuqian ZHU, Guoyi	150	9	141

COURSE INFO

VECTOR	[2-1-0:3]
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 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 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 course for the students who intend to master essential theoretical methods and practical 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 sciences. Grading Basis: Pass or Fail

VECTOR

[2-1-0:3]

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 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 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 course for the students who intend to master essential theoretical methods and practical 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 sciences. Grading Basis: Pass or Fail

MICS 6001M - Applied Time-Series Analysis (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6276)	Mo 09:00AM - 10:50AM	Rm 201, E1	LU, Zhonghai	30	8	22	0	Instructor Consent Required
T01 (6310)	Mo 11:00AM - 11:50AM	Rm 201, E1	LU, Zhonghai	30	8	22	0	Instructor Consent Required