YANG, Weikai - HKUST Class Schedule & Quota

Class Schedule & Quota
2026-27 Fall 2025-26 Summer 2025-26 Spring 2025-26 Winter
2026-27 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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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 MOES MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

DESCRIPTION	Data-centric AI provides a systematic introduction to the principles and methods for understanding, evaluating, and improving data in modern machine learning systems. This course explores how data quality, data value, data selection, data diagnosis, and data curation influence model behavior, generalization, and reliability. The first part of the course introduces foundational concepts in data quality assessment and error diagnosis, including methods for identifying noisy, inconsistent, incomplete, or unrepresentative data. The second part examines data valuation and selection, covering methodologies for estimating sample utility, constructing informative subsets, and optimizing data efficiency. The final part studies data curation and human-centered workflows, with emphasis on bias analysis, interactive and visual approaches, dataset maintenance, and applications to contemporary machine learning and large language model pipelines.

DESCRIPTION

Data-centric AI provides a systematic introduction to the principles and methods for understanding, evaluating, and improving data in modern machine learning systems. This course explores how data quality, data value, data selection, data diagnosis, and data curation influence model behavior, generalization, and reliability. The first part of the course introduces foundational concepts in data quality assessment and error diagnosis, including methods for identifying noisy, inconsistent, incomplete, or unrepresentative data. The second part examines data valuation and selection, covering methodologies for estimating sample utility, constructing informative subsets, and optimizing data efficiency. The final part studies data curation and human-centered workflows, with emphasis on bias analysis, interactive and visual approaches, dataset maintenance, and applications to contemporary machine learning and large language model pipelines.

DSAA 6000U - Data-Centric AI: Diagnosis, Valuation, and Curation (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6733)	Tu 01:00PM - 03:50PM	Rm 101, W1	YANG, Weikai	80	29	51	0

[Matching between Lecture & Lab required]

COURSE INFO

DESCRIPTION	This course covers programming and data structures using C++. In addition to basic programming concepts such as variables, arrays, pointers, and functions, students will learn about the standard data structures in C++, such as vectors, sets, maps, and queues. This course will also introduce the basics of object-oriented programming and some useful algorithms in the standard C++ libraries. Weekly laboratory experiments will provide hands-on experiences in programming.

DESCRIPTION

This course covers programming and data structures using C++. In addition to basic programming concepts such as variables, arrays, pointers, and functions, students will learn about the standard data structures in C++, such as vectors, sets, maps, and queues. This course will also introduce the basics of object-oriented programming and some useful algorithms in the standard C++ libraries. Weekly laboratory experiments will provide hands-on experiences in programming.

UFUG 2601 - C++ Programming (4 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L04 (6861)	MoWe 04:30PM - 05:50PM	Rm 147, E1	YANG, Weikai	60	22	38	0