WEI, Jiaheng - HKUST Class Schedule & Quota

Class Schedule & Quota
2025-26 Summer 2025-26 Spring 2025-26 Winter 2025-26 Fall
2025-26 Summer
Common Core Course
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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 BSBE CNGF DLED DSAA FUNH IPEN MSSM ROAS SMMG 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	Enrol	Avail	Wait	Remarks
L01 (6010)	15-JUN-2026 - 28-JUL-2026 TuTh 03:00PM - 05:50PM	Rm 101, E1	WEI, Jiaheng	80	0	80	0	> Add/Drop Deadline: 19 June 2026
LA01 (6011)	15-JUN-2026 - 29-JUL-2026 WeFr 02:00PM - 02:50PM	Rm 101, E1	WEI, Jiaheng	80	0	80	0