YANG, Kezhou - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Spring 2025-26 Winter 2025-26 Fall 2024-25 Summer
2025-26 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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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	[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	Wait	Remarks
L01 (6093)	Mo 10:00AM - 11:50AM	Rm 102, E4	GAN, Zecheng YANG, Kezhou YANG, Qichun	150	145	5	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course introduces the probabilistic computing system and the related underlying knowledge. The content includes the reviewing of the basic knowledge of probability, machine learning systems based on probabilistic computing as well as related device techniques that can be leveraged in constructing the hardware probabilistic computing systems. Through the course, the students will have a refresh of the knowledge of probability, and obtain the basic knowledge of the probabilistic machine learning models such as generative models for discrete data, linear and logistic regression models based on probability directed graphical models and mixture models. On the hardware side, the students will learn how probability distributions can be efficiently generated by novel devices, which the appropriate materials and device physics.

VECTOR

[3-0-0:3]

DESCRIPTION

This course introduces the probabilistic computing system and the related underlying knowledge. The content includes the reviewing of the basic knowledge of probability, machine learning systems based on probabilistic computing as well as related device techniques that can be leveraged in constructing the hardware probabilistic computing systems. Through the course, the students will have a refresh of the knowledge of probability, and obtain the basic knowledge of the probabilistic machine learning models such as generative models for discrete data, linear and logistic regression models based on probability directed graphical models and mixture models. On the hardware side, the students will learn how probability distributions can be efficiently generated by novel devices, which the appropriate materials and device physics.

MICS 6002H - Probability and Probabilistic Computing Systems (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6215)	Tu 01:30PM - 04:20PM	Rm 202, E4	YANG, Kezhou	20	7	13	0