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Taught Postgraduate Programme

Master of Science in Artificial Intelligence

 

Master of Science in Artificial Intelligence Poster

 

  • Candidates with a bachelor’s degree in subjects including but not limited to mathematics, statistics, computer science and engineering discipline in general are suitable to study the Master of Science in Artificial Intelligence programme.
  • University graduates and young professionals who aspire to pursue a career in this booming field
  • Scholastically superior students to pursue further studies in the relevant fields

Admission Requirements

To be eligible for admission to the programme, applicants shall:

1. hold a Bachelor's degree with honours of this University, or another qualification of equivalent standard from this University or another University or comparable institution acceptable for this purpose;

2. possess knowledge of linear algebra, calculus, probability theory, introductory statistics and computer programming; and

3. fulfil the University Entrance Requirements.

  • On-line application will be opened in early December 2021.
  • Main Round Deadline: 12:00nn, January 31, 2022. Candidates who apply within this period will have priority.
  • Clearing Round Deadline: 12:00nn, March 31, 2022.


Applications can be submitted via our on-line application system here.

The tuition fee for the programme is HK$260,000* for the 2022-23 intake. The fee shall normally be payable in three instalments over 1.5 years for full-time study.
 

In addition, students are required to pay Caution Money (HK$350), refundable on graduation subject to no claims being made, and Graduation Fee (HK$350).

* Subject to approval

Different types of scholarships will be offered.

 

Programme Information

Master of Science in Artificial Intelligence [MSc(AI)] is an interdisciplinary taught postgraduate programme jointly offered by the Department of Mathematics (host), the Department of Statistics & Actuarial Science and the Department of Computer Science. Its academic focus is promoting the applications of mathematics, statistics and computer science to facilitate AI in decision-making and problem-solving for various organizations and enterprises within the private and public sectors.

 

Course Highlights

The core courses of the proposed MSc(AI) programme will enable students to delve into the fundamental concepts, methodologies in artificial intelligence and the underlying mathematical and statistical tools with an effort to equip them with a solid foundation in both theory and practice. In the meantime, in order to make the programme the most comprehensive study in artificial intelligence, students can choose 18 credits of courses from the list of electives which cover a broad range of contemporary topics. The electives will provide students with solid training in diverse and applied techniques used in artificial intelligence.


Programme Curriculum

Commencing in September, the curriculum is composed of 72 credits of courses. If a student selects a course whose contents are similar to a course (or courses) which he/she has taken in his/her previous study, the Department may not approve the selection in question.

 

Compulsory Courses (42 credits)
ARIN7001 Foundations of artificial intelligence (6 credits)
ARIN7011  Optimization in artificial intelligence (6 credits)
ARIN7013

 

Numerical methods in artificial intelligence (6 credits)
ARIN7101 Statistics in artificial intelligence (6 credits)
ARIN7102  

 

Applied data mining and text analytics (6 credits)
COMP7404 Computational intelligence and machine learning (6 credits))
DASC7606 Deep learning (6 credits)

Disciplinary Electives (18 credits)*

with at least 6 credits from each of the following lists

List A

 

 

ARIN7014

 

Topics in advanced numerical analysis (6 credits)
ARIN7015 Topics in artificial intelligence and machine learning (6 credits)
MATH7224 Topics in advanced probability theory (6 credits)
MATH8502 Topics in applied discrete mathematics (6 credits)
MATH8503

 

Topics in advanced optimization (6 credits)
List B  
STAT6011 Computational statistics (6 credits)
STAT7008 Programming for data science (6 credits)
STAT8020 Quantitative strategies and algorithmic trading (6 credits)
STAT8021 Big data analytics (6 credits)
List C  
COMP7308 Introduction to unmanned systems (6 credits)
COMP7309 Quantum computing and artificial intelligence (6 credits)
COMP7409 Machine learning in trading and finance (6 credits)
COMP7502 Image processing and computer vision (6 credits)
ARIN7017 Legal issues in artificial intelligence and data science (6 credits)
* Students who have completed the same or similar courses in their previous studies may, on production of relevant transcripts, be permitted to select up to 18 credits of disciplinary electives from the other two lists if they are not able to find any untaken options from any one of the lists of disciplinary electives.
Capstone Project (12 credits)
ARIN7600 Artificial intelligence project (12 credits)

 

COURSE DESCRIPTION

Compulsory Courses

ARIN7001 Foundations of artificial intelligence (6 credits)

This course introduces foundational knowledge, methods and tools in mathematics, statistics and computer science for the purpose of studying and applying artificial intelligence.

 

Prerequisites: Nil

 

Assessment: coursework (50%) and examination (50%)

ARIN7011 Optimization in artificial intelligence (6 credits)

This course introduces students to the topics in theory and algorithms of optimization that play important roles in artificial intelligence and machine learning. Topics include: 1) Fundamental optimization models in AI (linear programming models, integer programming models, network models, reinforcement learning and deep learning models, etc.); 2) Optimization theory in AI (optimality conditions, constraint qualification, global landscape analysis of deep neural networks, P- and NP-hard problems, approximation algorithms, preliminary graph theory, etc.), 3) Optimization algorithms in AI: (a) Classic algorithms (simplex method, interior point method, branch and bound method, cutting plane method, representative algorithms, gradient type methods, CG methods, projection methods, penalty method, Lagrange methods, quasi-Newton methods, Newton type methods), (b) Stochastic algorithms (stochastic gradient descent (SGD), stochastic coordinate descent methods, subsampled Newton, stochastic quasi-Newton), (c) Algorithms for large-scale optimization problems (Operator splitting algorithms (BCD type algorithms, ADMM, primal-dual type algorithms, etc.), centralized/decentralized algorithms, etc.). (d) Algorithms for nonconvex optimization and training deep neural networks.

 

Prerequisites: Nil

 

Assessment: coursework (50%) and examination (50%)

ARIN7013 Numerical methods in artificial intelligence (6 credits)

This course introduces students to the numerical methods that are instrumental in artificial intelligence and machine learning. Topics include: 1) Notions and concepts in numerical analysis (convolution matrix (related to CNN), kernel methods, pattern analysis, direct methods for sparse matrices). 2) Numerical method for solving linear systems (Jacobi Method, Gauss-Seidel method, Cholesky decomposition, singular value decomposition (SVD), low-rank matrix approximation, with applications in artificial intelligence and machine learning). 3) Principal component analysis, tensor decomposition and their applications to computer vision, image processing and artificial intelligence and machine learning in general. 4) Compute eigenvalues and eigenvectors (Rayleigh quotient, with applications in artificial intelligence and machine learning). 5) Numerical methods for ordinary differential equations (stability, convergence analysis, relation between the SGD and Euler method, using DNN to compute ODEs).

 

Prerequisites: Nil

 

Assessment: coursework (50%) and examination (50%)

ARIN7101 Statistics in artificial intelligence (6 credits)

The development of artificial intelligence has revolutionized the theory and practice of statistical learning, while novel statistical learning approaches are becoming an integral part of artificial intelligence. By focusing on the interplay between statistical learning and artificial intelligence, this course reviews the main concepts underpinning classical statistical learning, studies computer-intensive methods for conducting statistical learning, and examines important issues concerning statistical learning drawn upon modern artificial intelligence technologies. Contents include classical frequentist and Bayesian inferences, resampling methods, large-scale hypothesis testing, regularization, and high-dimensional modeling.

 

Prerequisites: Nil

 

Assessment: coursework (40%) and examination (60%)

ARIN7102 Applied data mining and text analytics (6 credits)

With the rapid developments in computer and data storage technologies, the fundamental paradigms of classical data analysis are mature for change. Data mining aims at automated discovery of underlying structure and patterns in large amounts of data, especially text data. This course takes a practical approach to acquaint students with the new generation of data mining tools and techniques, and show how to use them to make informed decisions. Topics include data preparation, feature selection, association rules, decision trees, bagging, random forests and gradient boosting, cluster analysis, neural networks, introduction to text mining.

 

Prerequisites: Nil

 

Assessment: coursework (100%)

COMP7404 Computational intelligence and machine learning (6 credits)

This course will teach a broad set of principles and tools that will provide the mathematical, algorithmic and philosophical framework for tackling problems using Artificial Intelligence (AI) and Machine Learning (ML). AI and ML are highly interdisciplinary fields with impact in different applications, such as, biology, robotics, language, economics, and computer science. AI is the science and engineering of making intelligent machines, especially intelligent computer programs, while ML refers to the changes in systems that perform tasks associated with AI. Ethical issues in advanced AI and how to prevent learning algorithms from acquiring morally undesirable biases will be covered.

Topics may include a subset of the following: problem solving by search, heuristic (informed) search, constraint satisfaction, games, knowledge-based agents, supervised learning, unsupervised learning; learning theory, reinforcement learning and adaptive control and ethical challenges of AI and ML.

 

Pre-requisites: Nil, but knowledge of data structures and algorithms, probability, linear algebra, and programming would be an advantage.

 

Assessment: coursework (50%) and examination (50%)

DASC7606 Deep learning (6 credits)

Machine learning is a fast-growing field in computer science and deep learning is the cutting edge technology that enables machines to learn from large-scale and complex datasets. Ethical implications of deep learning and its applications will be covered and the course will focus on how deep neural networks are applied to solve a wide range of problems in areas such as natural language processing, and image processing. Other applications such as financial predictions, game playing and robotics may also be covered. Topics covered include linear and logistic regression, artificial neural networks and how to train them, recurrent neural networks, convolutional neural networks, generative models, deep reinforcement learning, and unsupervised feature learning.

 

Prerequisites: Basic programming skills, e.g., Python is required.

 

Assessment: coursework (40%) and examination (60%)

Disciplinary Electives

ARIN7014 Topics in advanced numerical analysis (6 credits)

This course covers a selection of topics in advanced numerical analysis which may include: 1) Krylov subspace, generalized minimal residual method (GMRES); 2) numerical (partial) differential equations; 3) stochastic methods and their applications to artificial intelligence and machine learning; 4) approximation theory, high-dimensional approximation (MC, QMC, sparse grid method); 5) Fourier analysis, wavelet analysis; 6) robust PCA and dimensional reduction methods. The selected topics may vary from year to year.

 

Prerequisites: Students should have basic knowledge in numerical analysis and scientific computing; pass in ARIN7013 Numerical methods in artificial intelligence or equivalent.

 

Assessment: coursework (50%) and examination (50%)

ARIN7015 Topics in artificial intelligence and machine learning (6 credits)

Selected topics in artificial intelligence that are of current interest will be discussed in this course.

 

Assessment: coursework (50%) and examination (50%)

MATH7224 Topics in advanced probability theory (6 credits)

Selected topics in probability theory will be discussed in this course.

 

Assessment: coursework (50%) and examination (50%)

MATH8502 Topics in applied discrete mathematics (6 credits)

This course aims to provide students with the opportunity to study some further topics in applied discrete mathematics. A selection of topics in discrete mathematics applied in combinatorics and optimization (such as algebraic coding theory, cryptography, discrete optimization, etc.) The selected topics may vary from year to year.

 

Pre-requisites: Knowledge in introductory discrete mathematics. Students may be asked to present appropriate evidence of having met the pre-requisites for enrolling in this course.

 

Assessment: coursework (50%) and examination (50%)

MATH8503 Topics in advanced optimization (6 credits)

A study in greater depth of some special topics in mathematical programming or optimization. It is mainly intended for students in Operations Research or related subject areas. This course covers a selection of topics which may include convex programming, nonconvex programming, saddle point problems, variational inequalities, optimization theory and algorithms suitable for applications in various areas such as machine learning, artificial intelligence, imaging and computer vision. The selected topics may vary from year to year.

 

Pre-requisites: Knowledge in introductory mathematical programming and optimization. Students may be asked to present appropriate evidence of having met the pre-requisites for enrolling in this course.

 

Assessment: coursework (100%)

STAT6011 Computational statistics (6 credits)

This course aims to give postgraduate students in statistics a background in modern computationally intensive methods in statistics. It emphasizes the role of computation as a fundamental tool of discovery in data analysis, of statistical inference, and for development of statistical theory and methods. Contents include: Bayesian statistics, Markov chain Monte Carlo methods including Gibbs sampler, the Metropolis-Hastings algorithm, and data augmentation; Generation of random variables including the inversion methods, rejection sampling, the sampling/importance resampling method; Optimization techniques including Newton’s method, expectation-maximization (EM) algorithm and its variants, and minorization-maximization (MM) algorithms; Integration including Laplace approximations, Gaussian quadrature, the importance sampling method, Numerical optimization and integration, EM algorithm and its variants, Simulation and Monte Carlo integration, Importance sampling and variance reduction techniques; and other topics such as Hidden Markov models, neural networks, and Bootstrap methods.

 

Pre-requisites: NIL

 

Assessment: coursework (50%) and examination (50%)

STAT7008 Programming for data science (6 credits)

In the big data era, it is very easy to collect huge amounts of data. Capturing and exploiting the important information contained within such datasets poses a number of statistical challenges. This course aims to provide students with a strong foundation in computing skills necessary to use R or Python to tackle some of these challenges. Possible topics to be covered may include exploratory data analysis and visualization, collecting data from a variety of sources (e.g. Excel, web-scraping, APIs and others), object-oriented programming concepts and scientific computation tools. Students will learn to create their own R packages or Python libraries.

 

Assessment: coursework (100%)

STAT8020 Quantitative strategies and algorithmic trading (6 credits)

Quantitative trading is a systematic investment approach that consists of identification of trading opportunities via statistical data analysis and implementation via computer algorithms. This course introduces various methodologies that are commonly employed in quantitative trading.

The first half of the course focuses at strategies and methodologies derived from the data snapshotted at daily or minute frequency. Some specific topics are: (1) techniques for trading trending and mean-reverting instruments, (2) statistical arbitrage and pairs trading, (3) detection of “time-series” mean reversion or stationarity, (4) cross-sectional momentum and contrarian strategies, (5) back-testing methodologies and corresponding performance measures, and (6) Kelly formula, money and risk management. The second half of the course discusses statistical models of high frequency data and related trading strategies. Topics that planned to be covered are: (7) introduction of market microstructure, (8) stylized features and models of high frequency transaction prices, (9) limit order book models, (10) optimal execution and smart order routing algorithms, and (11) regulation and compliance issues in algorithmic trading.

 

Pre-requisites: Students should have basic knowledge and experience in financial data analysis.

 

Assessment: coursework (50%) and examination (50%)

STAT8021 Big data analytics (6 credits)

The recent explosion of social media and the computerization of every aspect of life resulted in the creation of volumes of mostly unstructured data (big data): web logs, e-mails, videos, speech recordings, photographs, tweets and others. This course aims to provide students with knowledge and skills of some advanced analytics and statistical modelling for solving big data problems. Topics include recommender system, deep learning: CNN, RNN, LSTM, GRU, natural language processing, sentiment analysis and topic modeling. Students are required to possess basic understanding of Python language.

 

Pre-requisites: Pass in ARIN7102 Applied data mining and text analytics or equivalent

 

Assessment: coursework (100%)

COMP7308 Introduction to unmanned systems (6 credits)

This course is to study the theory and algorithms in unmanned systems. Topics include vehicle modelling, vehicle control, state estimation, perception and mapping, motion planning, and deep learning related techniques.

 

Assessment: coursework (40%) and examination (60%)

COMP7309 Quantum computing and artificial intelligence (6 credits)

This course offers an introduction to the interdisciplinary fields of quantum computation and quantum.

AI. The focus will lie on an accessible introduction to the elementary concepts of quantum mechanics, followed by a comparison between computer science and information science in the quantum domain.

The theoretical capability of quantum computers will be illustrated by analyzing fundamental algorithms of quantum computation and their potential applications in AI.

 

Assessment: coursework (60%) and examination (40%)

COMP7409 Machine learning in trading and finance (6 credits)

The course introduces our students to the field of Machine Learning, and help them develop skills of applying Machine Learning, or more precisely, applying supervised learning, unsupervised learning and reinforcement learning to solve problems in Trading and Finance.

This course will cover the following topics. (1) Overview of Machine Learning and Artificial Intelligence, (2) Supervised Learning, Unsupervised Learning and Reinforcement Learning, (3) Major algorithms for Supervised Learning and Unsupervised Learning with applications to Trading and Finance, (4) Basic algorithms for Reinforcement Learning with applications to optimal trading, asset management, and portfolio optimization, (5) Advanced methods of Reinforcement Learning with applications to high-frequency trading, cryptocurrency trading and peer-to-peer lending.

 

Assessment: coursework (65%) and examination (35%)

COMP7502 Image processing and computer vision (6 credits)

To study the theory and algorithms in image processing and computer vision. Topics include image representation; image enhancement; image restoration; mathematical morphology; image compression; scene understanding and motion analysis.

 

Assessment: coursework (50%) and examination (50%)

ARIN7017 Legal issues in artificial intelligence and data science (6 credits)

Modern information systems have had unprecedented impact on privacy while building dependency on them. The immense social benefits of such systems as data mining and cloud computing must be weighed against potential dangers with consideration of methods of mitigation of risk.

This course examines the growing legal, administrative, policy and technical issues associated with the use of artificial intelligence and information security and assurance. In particular, the relationship of data mining to information assurance and privacy are analyzed and legislative responses studied.

 

Assessment: coursework (100%)

Capstone Project

ARIN7600 Artificial intelligence project (12 credits)

The students will be required to attend an artificial intelligence ethics workshop and then carry out independent work on a major project under the supervision of staff members. A research report as well as an oral presentation on the research work and related ethics issues are required.

 

Assessment: research report (75%) and oral presentation (25%)

 

 

Programme Director

Prof Xiaoming Yuan

Professor Xiaoming Yuan
BSc, MPhil Nanjing U; PhD City U
Department of Mathematics

 

Staff List

Department of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial Science

Department of Mathematics

Department of Computer Science

  • Professor W K Ching, BSc, MPhil HK; PhD CUHK
  • Professor G Han, BSc, MSc Peking U; PhD Notre Dame
  • Dr B Kane, BSc, MSc Carnegie Mellon; PhD Wisconsin
  • Dr G Li, MS Fudan; PhD Texas A&M
  • Professor M K P Ng, MPhil HK; PhD CUHK
  • Dr Z Qu, BSc, MSc, PhD Ecole Polytechnique
  • Dr Y Wang, BSc NWPU; PhD TAMU
  • Dr T K Wong, BSc, MPhil CUHK; PhD NYU
  • Professor X Yuan, BSc, MPhil Nanjing U; PhD City U
  • Professor W Zang, BSc NUDT; MSc Academia Sinica; PhD Rutgers
  • Dr Z Zhang, BS, PhD Tsinghua
  • Dr K P Chow, MA, PhD UC Santa Barbara
  • Professor F Y L Chin, BASc Toronto; MSc, MA, PhD Princeton
  • Professor T Komura, PhD Tokyo
  • Dr L Kong, PhD Carnegie Mellon University
  • Dr P Luo, PhD CUHK
  • Dr J Pan, PhD North Carolina, Chapel Hill
  • Dr D Schnieders, PhD HK
  • Professor W Wang, BSc, MEng Shandong; PhD Alberta
  • Dr K K Y Wong, BEng CUHK; MPhil, PhD Cambridge
  • Professor Y Z Yu, PhD UC Berkeley

Department of Statistics and Actuarial Science

 
  • Dr Y Cao, BS Fudan; MS, PhD Princeton
  • Dr C W Kwan, BSc, PhD HK
  • Dr A S M Lau, BEng City; MSc HK; PhD CUHK
  • Dr E A L Li, BSc HK; MEcon, PhD Syd
  • Professor G D Li, BSc, MSc Peking; PhD HKU
  • Professor G S Yin, MA Temple; MSc, PhD N Carolina
 

 

     

    Enquiries

    Department of Mathematics

    The University of Hong Kong

    Faculty of Science

    The University of Hong Kong

    • G/F Chong Yuet Ming Physics Building
    • (852) 3917 5287
    • (852) 2858 4620