DSAA - HKUST Class Schedule & Quota

COURSE INFO

PRE-REQUISITE	DSAA 1001 or AIAA 2205
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.

PRE-REQUISITE

DSAA 1001 or AIAA 2205

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.

DSAA 2011 - Machine Learning (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6053)	14-JUL-2025 - 05-AUG-2025 TuWeThFr 03:00PM - 05:50PM	Rm 202, W2	ZHONG, Zixin	20	0	20	0	> Add/Drop Deadline: 16 July 2025
LA01 (6054)	14-JUL-2025 - 05-AUG-2025 TuWeThFr 10:00AM - 10:50AM	Rm 228, E1	ZHONG, Zixin	20	0	20	0

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	Wait	Remarks
L01 (6055)	16-JUN-2025 - 07-JUL-2025 MoTuThFr 09:00AM - 11:50AM	Rm 201, W4	XIE, Zeke	20	0	20	0	> Add/Drop Deadline: 17 June 2025
LA01 (6056)	16-JUN-2025 - 07-JUL-2025 MoTuThFr 02:00PM - 02:50PM	Rm 228, E1	XIE, Zeke	20	0	20	0

COURSE INFO

PRE-REQUISITE	(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND UFUG 2103 AND UFUG 2104
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.)

PRE-REQUISITE

(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND UFUG 2103 AND UFUG 2104

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 (6057)	16-JUN-2025 - 07-JUL-2025 MoTuThFr 01:30PM - 04:20PM	Rm 105, E3	DING, Ningning WANG, Wenjia	20	0	20	0	> Add/Drop Deadline: 17 June 2025
T01 (6058)	16-JUN-2025 - 07-JUL-2025 MoTuThFr 10:00AM - 10:50AM	Rm 105, E3	DING, Ningning WANG, Wenjia	20	0	20	0

COURSE INFO

VECTOR	[3-0-0:3]
DESCRIPTION	This course delves into the foundational theories and cutting-edge advancements in large language models (LLMs). The course emphasizes LLM's application in real-world scenarios, focusing on topics such as domain-specific LLMs (e.g., continuous pretraining, SFT, and RLHF), data-intensive and document-intensive scenarios (e.g., NL2SQL RAG). Active student participation is encouraged through mini-projects, presentations, and discussions, providing hands-on experience in leveraging LLMs for real-world challenges.

VECTOR

[3-0-0:3]

DESCRIPTION

This course delves into the foundational theories and cutting-edge advancements in large language models (LLMs). The course emphasizes LLM's application in real-world scenarios, focusing on topics such as domain-specific LLMs (e.g., continuous pretraining, SFT, and RLHF), data-intensive and document-intensive scenarios (e.g., NL2SQL RAG). Active student participation is encouraged through mini-projects, presentations, and discussions, providing hands-on experience in leveraging LLMs for real-world challenges.

DSAA 6000Q - Large Language Models: Theory and Practice (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6004)	16-JUN-2025 - 07-JUL-2025 MoTuWeTh 09:00AM - 11:50AM	Rm 102, W4	TANG, Jing TANG, Nan WANG, Wei	80	0	80	0	> Add/Drop Deadline: 17 June 2025 > Extended Drop Deadline: 19 June 2025

COURSE INFO

VECTOR	[3 credits]
PRE-REQUISITE	DSAA 6920 and DSAA 6921
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 III, students are required to complete a Final report with oral examination on the final output from the internship project and whether the student indeed knows how to apply AI techniques to concrete data science applications. This course is only available for MSc(DCAI) students. Graded PP, P or F.

DSAA 6922 - Industry Internship Ⅲ (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
R01 (6059)	TBA	No room required	TBA	60	0	60	0	> Add/Drop Deadline: 21 June 2025 > Extended Drop Deadline: 28 June 2025