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Research Postgraduate Programme

Programme Handbook

General Guidelines

As a multidisciplinary research institute equipped with a wide research network and membership across different Faculties, the Research Postgraduate programme at HKU Musketeers Foundation Institute of Data Science (“HKU IDS”) is designed to provide students with an opportunity to acquire greater expertise in data science through a combination of courses and intensive research under the supervision of a group of prominent researchers.

Basic requirement for a student is as follows:

  • to complete his/her research programme under the supervision of our HKU IDS scholar(s);
  • to present a written thesis which demonstrates profound knowledge and understanding in the candidate’s field of study;
  • to comply with supervision, attendance and all coursework requirements as laid down by the HKU IDS
  • to satisfy the University-wide graduation requirements.

Programme Structure

Regular courses for research postgraduate students admitted under HKU IDS mainly fall into the following three inter-related categories: 

  1. Foundation: what are the main problems that Data Science and Machine Intelligence try to address. 
  2. Computation: how do we develop effective and efficient methods to solve these problems. 
  3. Application: why and how are these methods can be applied or customized to real-world applications.  

In short, the above course categories aim to introduce to our HKU IDS students a wide spectrum of problems and applicable methods in the world of data science and machine learning.

1. Foundation

  • Fundamental structures, patterns, or models that are important for modern data science and machine intelligence: linear/nonlinear low-dim models, sequential or dynamical models, geometric and graphical models, and basic statistical or geometric properties of these models;
  • Problems associated with how to estimate such models from data, the associated statistical problems and computational problems; and how to estimate them in different settings: supervised, unsupervised;
  • How such models can be used for different purposes: classification, generation, prediction, and inference etc.

Below is the current list of courses with brief introduction: 

  • Theoretical Foundation of Deep Learning; Data Mining (regression/PCA for linear models, clustering and classification for mixture models, and introduction to more complex models such as manifolds or graphical models and the associated estimation and inference problems).  
  • High-Dimensional Data Analysis (mainly mixture low-dim linear models) 
  • Foundation of Sequential Decision Making (sequential, dynamical models) 
  • Statistical Inference and Machine Learning for Network Data (graphical or other structured models)

2. Computation

Courses in this category show when and how we can develop both effective (correct) and efficient (scalable) methods, including both algorithms and systems, for solving the above problems at scale.  

Below is the current list of courses with brief introduction: 

  • Optimization for Statistical Learning (sample complexity for the estimation problems to be well-defined, tractable, and even globally optimal, computational complexity in terms of scalable oracles, etc.) 
  • Scalable Optimization Methods in Data Science (first-order convex or nonconvex, distributed and parallel, stochastic etc.) 
  • Theoretical Foundation Foundations for Deep Learning (deep networks as unrolled optimization, deep learning as fine-tuning via back-propagation, etc.)

3. Application

Courses in this category teach the students how to apply fundamental models and computational methods to real-world domain-specific data and problems, including but not limited to visual data, natural languages, dynamical data, biological and medical data, financial data, as well as multi-modal data.  

Below is the current list of courses with brief introduction: 

  • Advanced Deep Learning for Computer Vision (a graduate counterpart for the undergraduate version on deep learning) 
  • Embodied AI for Robotics: Perception, Learning, and Action  
  • Advanced Natural Language Processing  

What Courses to Expect in the Future 

In the future, courses for other topics that are important for Data Science and Machine Intelligence will be offered, subject to the instructors’ availability. Such courses include but are not limited to, large-scale computational systems, data security and privacy, explainable AI, applications in medicine, applications in financial data, applications in social science, and many others. 

IDS seminars 

HKU IDS students are expected to attend a minimum of 10 seminars organized by the Institute per year. 

Programme Prerequisites

Each course has different prerequisites for admission. In general, students are required to complete undergraduate courses on Linear Algebra, Statistics or Machine Learning, and Optimization, before enrolling in the above courses. The students should be familiar with one programming language such as Python or C.   

Students will be assessed by their individual course instructor on their suitability to enroll in the particular course. The course instructor will evaluate your research ability and backgrounds based on your resumes and official transcripts submitted. Priority for course admission will be given to RPg students admitted under HKU IDS.

Graduation Requirement


MPhil 4-year PhD (full-time)
Registration dates 1 January OR 1 September First day of every month
Probationary period 12 months 18 months
Duration of study 24 months 48 months
No. of courses to be taken At least 3 At least 5

A candidate registered provisionally for the 2-year MPhil degree is subject to a probationary period of up to 12 months for full-time study. 

A candidate registered provisionally for the 4-year PhD (or MPhil/PhD) degree is subject to a probationary period of up to 18 months for full-time study. 

Before the end of probationary period, all MPhil and 4-year PhD candidates will be required to have satisfactorily completed all the Graduate School courses and at least 50% of the remaining prescribed coursework offered by the HKU IDS, by the end of the probationary period.  Failure to do so may lead to the termination of candidature.   

Coursework requirements 

The coursework component which encompasses both the Graduate School courses and Faculty/School/Departmental courses aims to enhance research postgraduate (RPg) students’ abilities in different stages of their research studies. Coursework is compulsory for all RPg students, unless approval for exemption has been granted. 

All students are required to take at minimum 3 courses to complete the HKU IDS programme, depending on what degrees they are taking.  

Course Requirements for MPhil Degrees 

  1. Undertaking at least three (3) regular courses, with one course from each of the categories (i.e., one course in Foundation, one course in Computation, and one course in Application); and 
  2. Attending a minimum (10) of the seminars organized by the IDS per year. 

Course Requirements for PhD Degrees 

  1. Undertaking at least five (5) courses, inclusive of both regular or elective courses offered by the HKU IDS (i.e., at least one course in Foundation, one course in Computation, one course in Application, and two courses from either one of the categories; and 
  2. Attending a minimum (10) of the seminars organized by the IDS per year.

Other Requirements 

PhD students will have to attend a Preliminary Examination for graduation. Typically, one course from each of the course category can be chosen by different groups as required courses for the Preliminary Examination.   

Courses offered by other departments that are highly related to the HKU IDS MPhil/PhD programme will be added to the list of courses to partly fulfil the graduation requirements, subject to the approval of the HKU IDS’ Programme Coordinator.   

Students are also required to fulfil coursework requirements stipulated by the HKU Graduate School. Please refer to the list of mandatory courses listed here. 

Course Assessment 

All courses taken by RPg students are assessed on a pass/fail basis according to the Graduate School’s policy. However, RPg students taking HKU IDS courses will be graded in letters, with a grade of B and above as “passing”.      

Students who fail a course at the first attempt may be required to undergo re-examination in the failed courses or to take an alternative course as determined by the HKU IDS Faculty Higher Degrees Committee (FHDC). Students who have not satisfied the examiners in a course (which, for this purpose, includes the alternative course) in the second attempt may be required to discontinue their studies.