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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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