ZHONG, Zixin - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Spring 2025-26 Winter 2025-26 Fall 2024-25 Summer
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

PRE-REQUISITE	(DSAA 1001 or AIAA 2205) OR (FTEC 3130 for FTEC Major Only)
DESCRIPTION	Machine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g., linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g., clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems. Basic knowledge on mathematics (e.g., basic of probability theory, linear algebra, calculus and optimization), programming (e.g., Python / C++ / Matlab) and data science are essential and will benefit the study of this course.

PRE-REQUISITE

(DSAA 1001 or AIAA 2205) OR (FTEC 3130 for FTEC Major Only)

DESCRIPTION

Machine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g., linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g., clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems. Basic knowledge on mathematics (e.g., basic of probability theory, linear algebra, calculus and optimization), programming (e.g., Python / C++ / Matlab) and data science are essential and will benefit the study of this course.

DSAA 2011 - Machine Learning (3 units)

Section	Date & Time	Room	Instructor	Quota	Avail
L01 (6435)	Th 09:00AM - 11:50AM	Rm 134, E1	YANG, Weikai ZHONG, Zixin	70	70
L02 (6436)	Mo 09:00AM - 11:50AM	Rm 122, E1	YANG, Weikai ZHONG, Zixin	70	70
LA01 (6437)	Th 07:00PM - 07:50PM	Rm 122, E1	YANG, Weikai ZHONG, Zixin	70	70
LA02 (6438)	Th 06:00PM - 06:50PM	Rm 122, E1	YANG, Weikai ZHONG, Zixin	70	70

COURSE INFO

PRE-REQUISITE	DSAA 2012
DESCRIPTION	Topics include history and motivations of reinforcement learning, tabular solution methods such as multi-arm bandits, finite Markov decision process, dynamic programming, monte carlo methods and temporal difference learning, approximate solution such as on-policy approximation of action values, and off-policy approximation.

DSAA 3053 - Introduction to Reinforcement Learning (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6413)	Tu 03:00PM - 05:50PM	Room 521H VR Room, W1	ZHONG, Zixin	20	0	20	0
LA01 (6414)	Th 02:00PM - 02:50PM	Rm 228, E1	ZHONG, Zixin	20	0	20	0