SDST4011 Natural language processing (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 15
Course Co-ordinator Dr A S M Lau, SCDS (Department of Statistics and Actuarial Science) < adelalau@hku.hk >
Teachers Involved (Dr A S M Lau,Statistics & Actuarial Science)
Course Objectives Natural language processing (NLP) is a subfield of artificial intelligence, focusing on understanding human language. In essence, NLP is interested in building a tool that can use language like humans. This course will introduce the mathematical, statistical and computational challenges in natural language processing. It covers main applications of NLP techniques and a range of models in structured prediction and deep learning. In this course, students will gain a thorough introduction to cutting-edge machine learning and deep learning techniques for NLP.
Course Contents & Topics This course covers a broad range of topics in natural language processing (NLP), including text classification, sentiment analysis, neural network, word embedding, sequence models, language models, machine translation, topic detection, chatGPT. The underlying techniques from probability, statistics, machine learning, transformer and deep learning will also be introduced.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 learn about the techniques behind modern NLP
CLO 2 implement basic algorithms and methods on real-world data
CLO 3 gain hands-on experience on building NLP models
CLO 4 learn backgrounds to understand current research
CLO 5 get exposed to linguistic concepts and tasks in NLP
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST2602 and (COMP2113 or COMP2119 or COMP2396); and
Not for students who have passed in APAI4011, or already enrolled in this course.
Recommended: familiarity with deep learning or machine learning; strong programming skills (e.g., Python)
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
Course to PLO Mapping 2024 Major in Decision Analytics < PLO 1,2,3,4 >
2023 Major in Decision Analytics < PLO 1,2,3,4 >
2022 Major in Decision Analytics < PLO 1,2,3,4 >
2021 Major in Decision Analytics < PLO 1,2,3,4 >
Offer in 2025 - 2026 Y        1st sem    Examination No Exam     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments and tutorials) 30.0 1,2,3
Project reports 40.0 1,2,3,4,5
Test 30.0 1,2,3
Required/recommended reading
and online materials
Course Website http://moodle.hku.hk
Additional Course Information