Courses Offered

COMP3520 Special Topics in Data Science

2025-26

Instructor(s):

Huang Chao

(Class A)

No. of credit(s):

Recommended Learning Hours:

Lecture:	27.0
Tutorial:	9.0

Pre-requisite(s):

COMP1117 or COMP2113 or MATH1013

Co-requisite(s):

Mutually exclusive with:

Remarks:

Course Learning Outcomes

1.	Apply advanced machine learning and deep learning techniques to solve complex data science problems.
2.	Develop and deploy large language model applications, including prompt engineering, fine-tuning techniques, and integration strategies for domain-specific use cases.
3.	Design and implement multi-modal data processing systems that integrate diverse data types (text, images, audio, structured data) to extract meaningful insights and patterns.
4.	Create autonomous agents capable of intelligent decision-making and task execution in dynamic environments using appropriate AI frameworks and methodologies.

Mapping from Course Learning Outcomes to Programme Learning Outcomes
	PLO a	PLO b	PLO c	PLO d	PLO f	PLO h	PLO i
CLO 1	T,P	T,P	T,P	T,P	T,P	T,P	T,P
CLO 2	T,P	T,P	T,P	T,P		T,P	T,P
CLO 3	T,P	T,P	T,P	T,P		T,P	T,P
CLO 4	T,P	T,P	T,P	T,P		T,P	T,P

T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to here.

Syllabus

Calendar Entry:
“Special Topics in Data Science” is an advanced course designed to explore cutting-edge and specialized areas within the rapidly evolving field of data science. The course covers contemporary topics such as deep learning, natural language processing, multi-modal learning, large language models, autonomous agents, and emerging techniques in machine learning and artificial intelligence. Students will examine real-world applications, analyze recent research papers, and work on hands-on projects using state-of-the-art tools and frameworks. The course aims to provide students with in-depth knowledge of current trends and methodologies in data science, develop critical thinking skills for evaluating new technologies, and prepare them to tackle complex, domain-specific challenges in their future careers or research endeavors.

Detailed Description:

Advanced Machine Learning Algorithms and Deep Learning Architectures	Mapped to CLOs
	1
Large Language Models: Implementation, Fine-tuning, and Prompt Engineering	Mapped to CLOs
	2
Multi-modal Data Processing and Integration Techniques	Mapped to CLOs
	3
Autonomous Agent Design and Development Frameworks	Mapped to CLOs
	4

Assessment:
Continuous Assessment: 60%
Written Examination: 40%

Teaching Plan

Please refer to the corresponding Moodle course.

Moodle Course(s)

Courses Offered

COMP3520 Special Topics in Data Science

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