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 (6596)	MoWe 10:30AM - 11:50AM	Rm 134, E1	LI, Lei WANG, Wei ZHANG, Yongqi	70	59	11	0
LA01 (6597)	Mo 12:00PM - 12:50PM	Rm 134, E1	LI, Lei WANG, Wei ZHANG, Yongqi	70	59	11	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
L01 (6659)	Th 09:00AM - 11:50AM	Rm 134, E1	JIN, Tianyuan ZHONG, Zixin	70	27	43
L02 (6660)	Tu 10:30AM - 01:20PM	Rm 122, E1	JIN, Tianyuan ZHONG, Zixin	70	35	35
LA01 (6661)	Fr 03:00PM - 03:50PM	Rm 122, E1	JIN, Tianyuan ZHONG, Zixin	70	51	19
LA02 (6662)	Fr 04:00PM - 04:50PM	Rm 122, E1	JIN, Tianyuan ZHONG, Zixin	70	11	59

COURSE INFO

PRE-REQUISITE	DSAA 2011 or AIAA 3111
DESCRIPTION	This course provides students with an extensive exposure to deep learning. Topics include shallow and deep neural networks, activation functions and rectified linear unit, construction of deep neural networks and matrix representations including deep convolutional neural networks and deep recursive neural networks, computational issues including backpropagation, automatic differentiation, stochastic gradient descent, complexity analysis, approximation analysis including universality of approximation, design of deep neural network architectures and programming according to various applications.

PRE-REQUISITE

DSAA 2011 or AIAA 3111

DESCRIPTION

This course provides students with an extensive exposure to deep learning. Topics include shallow and deep neural networks, activation functions and rectified linear unit, construction of deep neural networks and matrix representations including deep convolutional neural networks and deep recursive neural networks, computational issues including backpropagation, automatic differentiation, stochastic gradient descent, complexity analysis, approximation analysis including universality of approximation, design of deep neural network architectures and programming according to various applications.

DSAA 2012 - Deep Learning (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6666)	Mo 03:00PM - 04:20PM	Rm 148, E1	GUO, Zhijiang XIA, Jun	48	30	18
L01 (6666)	Fr 10:30AM - 11:50AM	Rm 148, E1	GUO, Zhijiang XIA, Jun	48	30	18
L02 (6667)	MoWe 09:00AM - 10:20AM	Rm 101, W4	GUO, Zhijiang XIA, Jun	48	48	0
LA01 (6668)	Th 05:30PM - 06:20PM	Rm 148, E1	GUO, Zhijiang XIA, Jun	48	47	1
LA02 (6669)	Th 05:30PM - 06:20PM	Rm 101, W4	GUO, Zhijiang XIA, Jun	48	31	17

COURSE INFO

PRE-REQUISITE	UFUG 2602
DESCRIPTION	Design and Analysis of Algorithms is an important course that bridges students to a number of advanced courses in data science and analytics. This course introduces core data structures and algorithms. It covers advanced asymptotic complexity analysis, introduces common algorithmic paradigms (e.g., divide-and-conquer, greedy, and dynamic programming), a collection of classic algorithms (e.g., graph algorithms) and introduces the computational complexity theory. The course employs a range of assessment methods, including individual projects, coding exercises and closed-book exams, to foster both theoretical and practical foundation.

PRE-REQUISITE

UFUG 2602

DESCRIPTION

Design and Analysis of Algorithms is an important course that bridges students to a number of advanced courses in data science and analytics. This course introduces core data structures and algorithms. It covers advanced asymptotic complexity analysis, introduces common algorithmic paradigms (e.g., divide-and-conquer, greedy, and dynamic programming), a collection of classic algorithms (e.g., graph algorithms) and introduces the computational complexity theory. The course employs a range of assessment methods, including individual projects, coding exercises and closed-book exams, to foster both theoretical and practical foundation.

DSAA 2043 - Design and Analysis of Algorithms (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6701)	MoWe 10:30AM - 11:50AM	Rm 101, E1	ZHANG, Yanlin	70	47	23
L02 (6702)	TuTh 01:30PM - 02:50PM	Rm 134, E1	LU, Shangqi	70	75	0
LA01 (6703)	Th 03:30PM - 04:20PM	Rm 227, E1	LU, Shangqi ZHANG, Yanlin	48	39	9
LA02 (6704)	Th 04:30PM - 05:20PM	Rm 227, E1	LU, Shangqi ZHANG, Yanlin	48	37	11
LA03 (6705)	Tu 03:00PM - 03:50PM	Rm 227, E1	LU, Shangqi ZHANG, Yanlin	44	46	0

COURSE INFO

PRE-REQUISITE	DSAA 1001 OR AIAA 2205
DESCRIPTION	In this course, students will work in teams to design, implement, and deliver a software system that addresses a real-world data science problem. Projects will be sourced from domains such as finance, healthcare, transportation, and manufacturing. Each team will apply data science tools and techniques to develop functional software solutions, integrating data-driven models into real applications. The course also introduces agile project management, teamwork, and communication skills. Students will work from requirements through to implementation and delivery, following a structured process with a focus on collaboration, iteration, and clear reporting.

PRE-REQUISITE

DSAA 1001 OR AIAA 2205

DESCRIPTION

In this course, students will work in teams to design, implement, and deliver a software system that addresses a real-world data science problem. Projects will be sourced from domains such as finance, healthcare, transportation, and manufacturing. Each team will apply data science tools and techniques to develop functional software solutions, integrating data-driven models into real applications. The course also introduces agile project management, teamwork, and communication skills. Students will work from requirements through to implementation and delivery, following a structured process with a focus on collaboration, iteration, and clear reporting.

DSAA 2044 - Data Science Project (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6636)	Fr 12:00PM - 02:50PM	Rm 134, E1	DING, Zishuo	60	46	14	0
LA01 (6637)	Fr 05:00PM - 05:50PM	Rm 122, E1	DING, Zishuo	60	46	14	0

COURSE INFO

PRE-REQUISITE	(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND (UFUG 2102 OR UFUG 2103) AND UFUG 2104
EXCLUSION	AIAA 2711
DESCRIPTION	Topics include: advanced linear algebra, advanced geometry (e.g., manifold), vector calculus, advanced mathematics models (regression, latent-variable model) etc.. Also includes topics related to discrete mathematics (e.g., enumeration techniques, basic number theory, logic and proofs, recursion and recurrences, probability theory and graph theory. The approach of this course is specifically computer science application oriented.)

DSAA 2088 - Mathematics for Data Science (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6598)	We 01:30PM - 04:20PM	Rm 233, W1	DING, Ningning	40	31	9	0
T01 (6599)	We 04:30PM - 05:20PM	Rm 233, W1	DING, Ningning	40	31	9	0

COURSE INFO

PRE-REQUISITE	DSAA 2031 AND UFUG 2601
DESCRIPTION	This course introduces the fundamentals of high-performance and parallel computing. It targets at scientists, engineers, scholars, and everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. The course covers the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code.

PRE-REQUISITE

DSAA 2031 AND UFUG 2601

DESCRIPTION

This course introduces the fundamentals of high-performance and parallel computing. It targets at scientists, engineers, scholars, and everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. The course covers the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code.

DSAA 3032 - Introduction to High-Performance and Parallel Computing (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6600)	Mo 09:00AM - 11:50AM	Rm 102, E1	WEN, Zeyi	40	14	26	0
LA01 (6601)	Mo 04:30PM - 05:20PM	Rm 227, E1	WEN, Zeyi	40	14	26	0

COURSE INFO

PRE-REQUISITE	DSAA 2012
EXCLUSION	AIAA 4051
DESCRIPTION	This course provides a comprehensive overview of modern approaches for (1) natural language processing and (2) knowledge graphs. Students will explore text processing, linguistic preprocessing, and foundational language models, progressing from ngrams to word embeddings and neural language models. The curriculum covers semantic and discourse analysis, information extraction, and the integration of knowledge bases. Key topics include knowledge-intensive tasks, and the construction and application of knowledge graphs, along with ontologies and semantic web technologies like RDF, OWL, and SPARQL. The course also delves into large language models, emphasizing transformer architectures and ethical considerations of NLP and knowledge systems such as bias, fairness, and privacy. Through lectures, discussions, and practical exercises, students will gain critical insights into the development, maintenance, and application of NLP and knowledge systems, preparing them to address real-world challenges in these fields.

DSAA 3051 - Introduction to Natural Language Processing and Knowledge Engineering (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6602)	MoWe 12:00PM - 01:20PM	Rm 102, E1	WEI, Jiaheng	40	22	18	0
T01 (6603)	We 01:30PM - 02:20PM	Rm 102, E1	WEI, Jiaheng	40	22	18	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 (6604)	Mo 03:00PM - 05:50PM	Rm 202, E3	WANG, Wei	40	8	32	0
T01 (6605)	Tu 09:00AM - 09:50AM	Rm 202, E3	WANG, Wei	40	8	32	0

COURSE INFO

PRE-REQUISITE	(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND (UFUG 2106 OR DSAA 2088)
DESCRIPTION	Advances in machine learning and deep machine learning are continuously penetrating computational science and engineering. This course will introduce students to recent advances in data-driven scientific research based on deep learning techniques, enabling next-generation scientists and engineers to leverage AI in crossdisciplinary fields. Topics include but not limited to advanced deep neural network models, physicsinformed neural networks (PINN), model discovery and prediction, uncertainty quantification, and neural operators. Applications to diverse scientific and engineering computational tasks aided by deep learning methods will also be introduced.

PRE-REQUISITE

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

DESCRIPTION

Advances in machine learning and deep machine learning are continuously penetrating computational science and engineering. This course will introduce students to recent advances in data-driven scientific research based on deep learning techniques, enabling next-generation scientists and engineers to leverage AI in crossdisciplinary fields. Topics include but not limited to advanced deep neural network models, physicsinformed neural networks (PINN), model discovery and prediction, uncertainty quantification, and neural operators. Applications to diverse scientific and engineering computational tasks aided by deep learning methods will also be introduced.

DSAA 3074 - Deep Learning for Science and Engineering (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6618)	Th 12:00PM - 02:50PM	Rm 222, W1	LAI, Zhilu	40	13	27	0
T01 (6619)	Fr 10:30AM - 11:20AM	Rm 222, W1	LAI, Zhilu	40	13	27	0

COURSE INFO

PRE-REQUISITE	DSAA 2011 OR DSAA 2012
DESCRIPTION	In this course, topics include: SVM, kernel methods, ensemble learning, dimensionality reduction, Gaussian Mixture Models, Hidden Markov Models, Topic Models, semi-supervised learning, transfer learning, and domain adaptation. This course extends classical ML and deep learning with a few advanced themes: kernel-based methods; ensemble and representation learning; probabilistic modeling and latent variable methods; learning beyond labels, including semi-, self-, and unsupervised approaches, and out-of-distribution learning. Students will learn to design, analyze, and implement these advanced learning approaches as well as handle non-typical learning scenarios.

PRE-REQUISITE

DSAA 2011 OR DSAA 2012

DESCRIPTION

In this course, topics include: SVM, kernel methods, ensemble learning, dimensionality reduction, Gaussian Mixture Models, Hidden Markov Models, Topic Models, semi-supervised learning, transfer learning, and domain adaptation. This course extends classical ML and deep learning with a few advanced themes: kernel-based methods; ensemble and representation learning; probabilistic modeling and latent variable methods; learning beyond labels, including semi-, self-, and unsupervised approaches, and out-of-distribution learning. Students will learn to design, analyze, and implement these advanced learning approaches as well as handle non-typical learning scenarios.

DSAA 4011 - Advanced Machine Learning and Deep Learning (4 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6620)	Th 09:00AM - 11:50AM	Rm 233, W1	HUANG, Yuwen	40	8	32	0
LA01 (6621)	Fr 03:00PM - 03:50PM	Rm 233, W1	HUANG, Yuwen	40	8	32	0

COURSE INFO

PRE-REQUISITE	DSAA 2031
DESCRIPTION	This course is an advanced course for understanding the diversity of data types (e.g., graph data, time series data, spatial data, video data) in data science: why they exist, how to store them, how to query them, and how to build scalable and efficient solutions for managing these data types. It will also introduce how to make predictions, especially for time-series data, which has important applications for financial data and health care data.

PRE-REQUISITE

DSAA 2031

DESCRIPTION

This course is an advanced course for understanding the diversity of data types (e.g., graph data, time series data, spatial data, video data) in data science: why they exist, how to store them, how to query them, and how to build scalable and efficient solutions for managing these data types. It will also introduce how to make predictions, especially for time-series data, which has important applications for financial data and health care data.

DSAA 4032 - Complex Data Management (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6622)	We 09:00AM - 11:50AM	Rm 222, W1	LUO, Yuyu	40	18	22	0

COURSE INFO

PRE-REQUISITE	DSAA 2011 or DSAA 2012 or AIAA 3111 or DSAA4040
CO-REQUISITE	DLED 4020
DESCRIPTION	This course is an independent study or project under faculty guidance on a data science and analytics related topic. Students will complete a written report, presentation, and/or examination, while practicing English skills (reading, writing, understanding, presentation) via project-related activities. The course features the use of AI-assisted tools to broaden the scope of projects, enrich the technical contribution, and enable students to address scientific or real-world problems more effectively and efficiently. Credit load will be spread over the year, fostering independent research capabilities and proficiency in combining data science techniques with Al-assisted tools. Graded: Letter grade or PP.

DSAA 4591 - Final Year Capstone Project (6 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6623)	TBA	No room required	TBA	35	35	0	0

COURSE INFO

DESCRIPTION	With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI.

DSAA 5002 - Data Mining and Knowledge Discovery in Data Science (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6168)	Th 03:00PM - 05:50PM	Lecture Hall C	LI, Jia	130	130	0	0
L02 (6169)	Tu 03:00PM - 05:50PM	Lecture Hall A	YU, JEFFREY XU	120	114	6	0

COURSE INFO

DESCRIPTION	In this course, the concepts and implementation schemes in advanced database management systems for data science applications will be introduced, such as disk and memory management, advanced access methods, implementation of relational operators, query processing and optimization, transactions and concurrency control. It also introduces emerging database related techniques for data science.

DSAA 5012 - Advanced Database Management for Data Science (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6170)	Fr 01:30PM - 04:20PM	Rm 101, E1	LI, Lei	93	93	0	0

COURSE INFO

DESCRIPTION	This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.

DESCRIPTION

This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.

DSAA 5020 - Foundation of Data Science and Analytics (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6173)	Mo 09:00AM - 11:50AM	Rm 101, W1	TANG, Jing	155	155	0	0

COURSE INFO

DESCRIPTION	This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.

DESCRIPTION

This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.

DSAA 5024 - Data Exploration and Visualization (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6177)	Fr 09:00AM - 11:50AM	Rm 149, E1	ZENG, Wei	60	60	0	0

COURSE INFO

DESCRIPTION	Game theory offers powerful insights into strategic decision-making, with promising applications in fields such as human-AI interaction, multi-agent systems, adversarial machine learning, and social networks. This course introduces the core concepts of game theory, focusing on the study of interactions among rational decision-makers. Students will explore foundational topics such as utility, Nash equilibrium, dominant strategies, mixed strategies, and both static and dynamic games. Through practical examples, the course will demonstrate how game theory is applied to optimize strategies in artificial intelligence, network systems, economics, and our daily life. By the end of the course, students will be equipped to analyze and model interactions, making it a vital tool for modern problem-solving in AI, data science, and beyond.

DESCRIPTION

Game theory offers powerful insights into strategic decision-making, with promising applications in fields such as human-AI interaction, multi-agent systems, adversarial machine learning, and social networks. This course introduces the core concepts of game theory, focusing on the study of interactions among rational decision-makers. Students will explore foundational topics such as utility, Nash equilibrium, dominant strategies, mixed strategies, and both static and dynamic games. Through practical examples, the course will demonstrate how game theory is applied to optimize strategies in artificial intelligence, network systems, economics, and our daily life. By the end of the course, students will be equipped to analyze and model interactions, making it a vital tool for modern problem-solving in AI, data science, and beyond.

DSAA 6000P - Game Theory (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6734)	Th 01:30PM - 04:20PM	Rm 122, E1	DING, Ningning	71	51	20	0

COURSE INFO

DESCRIPTION	Cloud computing provides the foundation for modern large-scale systems, while recent advances in AI, especially LLMs, are reshaping how such systems are executed and managed. This course studies AI infrastructure from a systems perspective, focusing on how cloud systems support AI workloads. By treating AI as a new class of workload, the course examines how system abstractions evolve to accommodate GPU-centric, stateful, and performance-critical environments. In particular, this course uses a unified framework of AI infrastructure along four key dimensions: execution, state, resource, and optimization. Through this structured perspective, students will learn how classical system principles extend to modern AI systems and will be prepared to analyze and design next-generation AI infrastructure.

DESCRIPTION

Cloud computing provides the foundation for modern large-scale systems, while recent advances in AI, especially LLMs, are reshaping how such systems are executed and managed. This course studies AI infrastructure from a systems perspective, focusing on how cloud systems support AI workloads. By treating AI as a new class of workload, the course examines how system abstractions evolve to accommodate GPU-centric, stateful, and performance-critical environments. In particular, this course uses a unified framework of AI infrastructure along four key dimensions: execution, state, resource, and optimization. Through this structured perspective, students will learn how classical system principles extend to modern AI systems and will be prepared to analyze and design next-generation AI infrastructure.

DSAA 6000T - Cloud and AI Infrastructure Systems (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6732)	Th 10:00AM - 12:50PM	Lecture Hall B	TANG, Guoming	100	81	19	0

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

COURSE INFO

DESCRIPTION	This course provides students who lack neural network background with an extensive exposure to modern neural networks. Topics include modern neural network architectures, deep learning theory, training, optimization, regularization, foundation models, deep generative models, transfer learning, and practice experience in the wild.

DSAA 6000V - Modern Neural Networks (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6996)	Th 06:00PM - 08:50PM	Rm 228, E2	XIE, Zeke	80	68	12	0

DESCRIPTION	In this course, an independent research project will be carried out under the supervision of a faculty member.

DSAA 6018A - Independent Study (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6798)	TBA	No room required	TBA	30	1	29	0	Instructor Consent Required

DESCRIPTION	In this course, an independent research project will be carried out under the supervision of a faculty member.

DSAA 6018B - Independent Study (1 unit)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6799)	TBA	No room required	TBA	30	6	24	0	Instructor Consent Required

COURSE INFO

DESCRIPTION	This course will teach students practical programming and parallel processing skills on implementing various deep learning or machine learning models, starting from preparing data, feature selection to model choosing, hyperparameter tuning, and final result analysis and explaining. This course is only available for MSc(DCAI) students.

DSAA 6100 - Practical Lab Course (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail
L01 (6185)	Tu 01:30PM - 02:50PM	Rm 102, E4	LUO, Yuyu	100	99	1
LA01 (6186)	Tu 03:00PM - 05:50PM	Rm 102, E4	LUO, Yuyu	100	99	1
LA01 (6186)	Tu 07:00PM - 09:50PM	Rm 102, E4	LUO, Yuyu	100	99	1

COURSE INFO

DESCRIPTION	In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state of the art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F.

DSAA 6101 - Data Science and Analytics Program Seminar I (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6196)	Tu 11:00AM - 11:50AM	Lecture Hall B	TANG, Nan	150	129	21	0

COURSE INFO

DESCRIPTION

In this course, students will be trained in the industry. They will work under the guidance of their supervisors (industry and academia) to practice what they have learned in the program, and apply the data science and AI knowledge and techniques to various real-life problems. In Part I, students are required to complete an Open Topic report with oral examination on the scientific value, feasibility and technical challenges of the proposed topic. This course is only available for MSc(DCAI) students. Graded PP, P or F.

DSAA 6920 - Industry Internship Ⅰ (2 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6197)	TBA	No room required	TBA	100	91	9	0

COURSE INFO

DESCRIPTION	Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

DSAA 6990 - MPhil Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6198)	TBA	No room required	TBA	100	73	27	0

COURSE INFO

DESCRIPTION	Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

DSAA 7990 - Doctoral Thesis Research (0 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6200)	TBA	No room required	TBA	100	62	38	0