DSAA - HKUST Class Schedule & Quota

COURSE INFO

DESCRIPTION	Data science changes the way people process data in different areas. It has promoted the development of many subjects. This course introduces beginners to the whole lifecycle of data science problems and solutions. The course will help students comprehensively understand the basic knowledge of data science and use computer techniques to handle the real-life data science problems. Topics covered include data collection and processing, machine learning (including classification and clustering), statistical estimation and inference methods, and case studies.

DESCRIPTION

Data science changes the way people process data in different areas. It has promoted the development of many subjects. This course introduces beginners to the whole lifecycle of data science problems and solutions. The course will help students comprehensively understand the basic knowledge of data science and use computer techniques to handle the real-life data science problems. Topics covered include data collection and processing, machine learning (including classification and clustering), statistical estimation and inference methods, and case studies.

DSAA 1001 - Introduction to Data Science and Analytics (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6008)	15-JUN-2026 - 28-JUL-2026 TuTh 09:00AM - 11:50AM	Rm 101, E1	LI, Lei WANG, Wei ZHANG, Yongqi	80	0	80	0	> Add/Drop Deadline: 18 June 2026
LA01 (6009)	15-JUN-2026 - 28-JUL-2026 TuTh 06:00PM - 06:50PM	Rm 101, E1	LI, Lei WANG, Wei ZHANG, Yongqi	80	0	80	0

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

COURSE INFO

PRE-REQUISITE	UFUG 2601
DESCRIPTION	This course introduces the basic mechanisms of computer hardware of modern computers, and covers operating systems fundamentals such as memory systems, input-output systems, interrupts and exceptions, pipelining, performance and cost analysis. Students will learn the purpose and structure of operating systems, as well as the basic knowledge on process management, CPU scheduling, file systems, security and protection.

PRE-REQUISITE

UFUG 2601

DESCRIPTION

This course introduces the basic mechanisms of computer hardware of modern computers, and covers operating systems fundamentals such as memory systems, input-output systems, interrupts and exceptions, pipelining, performance and cost analysis. Students will learn the purpose and structure of operating systems, as well as the basic knowledge on process management, CPU scheduling, file systems, security and protection.

DSAA 2042 - Computer Architecture and Systems (4 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6018)	15-JUN-2026 - 28-JUL-2026 TuTh 12:00PM - 02:50PM	Rm 222, W1	WEN, Zeyi	40	0	40	0	> Add/Drop Deadline: 18 June 2026
LA01 (6019)	15-JUN-2026 - 28-JUL-2026 TuTh 07:00PM - 07:50PM	Rm 222, W1	WEN, Zeyi	40	0	40	0

COURSE INFO

PRE-REQUISITE	(DSAA 2011 or DSAA 2012 or AIAA 3111) AND (UFUG 2106 or DSAA 2088)
DESCRIPTION	This course provides a cohesive introduction to statistical machine learning and scalable data analysis. Students will develop intuition for learning theory, modern predictive modeling, and algorithmic techniques for working with large datasets. Balancing fundamentals with applied methodology, the course connects theory, algorithms, and systems considerations to support reliable learning at scale. Assessments include a midterm and final exam, with hands-on components that reinforce conceptual understanding and practical skills.

PRE-REQUISITE

(DSAA 2011 or DSAA 2012 or AIAA 3111) AND (UFUG 2106 or DSAA 2088)

DESCRIPTION

This course provides a cohesive introduction to statistical machine learning and scalable data analysis. Students will develop intuition for learning theory, modern predictive modeling, and algorithmic techniques for working with large datasets. Balancing fundamentals with applied methodology, the course connects theory, algorithms, and systems considerations to support reliable learning at scale. Assessments include a midterm and final exam, with hands-on components that reinforce conceptual understanding and practical skills.

DSAA 3073 - Theories in Data Science (4 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6014)	15-JUN-2026 - 27-JUL-2026 MoWe 10:30AM - 01:20PM	Rm 222, W1	WANG, Wei	40	0	40	0	> Add/Drop Deadline: 17 June 2026
T01 (6015)	15-JUN-2026 - 27-JUL-2026 MoWe 06:00PM - 06:50PM	Rm 222, W1	WANG, Wei	40	0	40	0

COURSE INFO

PRE-REQUISITE	DSAA 2031 AND DSAA 2042
DESCRIPTION	This course introduces basics on machine learning systems. Students will study efficient machine learning systems for training and inference, and general design principles. The general issues including privacy, security and fairness of machine learning systems are also covered in this course. The course also aims to discuss machine learning for general systems, and how to design efficient machine learning systems by parallelism and distributed computing.

PRE-REQUISITE

DSAA 2031 AND DSAA 2042

DESCRIPTION

This course introduces basics on machine learning systems. Students will study efficient machine learning systems for training and inference, and general design principles. The general issues including privacy, security and fairness of machine learning systems are also covered in this course. The course also aims to discuss machine learning for general systems, and how to design efficient machine learning systems by parallelism and distributed computing.

DSAA 4012 - Machine Learning Systems (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6013)	15-JUN-2026 - 27-JUL-2026 MoWe 03:00PM - 05:50PM	Rm 202, W4	DUAN, Huayi	40	0	40	0	> Add/Drop Deadline: 17 June 2026