Description of Postgraduate Courses -- Research Postgraduate Course Sharing Scheme (Spring Term 2020-2021)

Level of Courses

All courses offered in this scheme are at postgraduate level.

Course Vector and Credits

Each course is assigned a course vector which indicates the number of instructional hours required and credits to be earned.  The course vector is presented in the form of [L-T-Lab:C] where

      L = lecture hours per week
      T = tutorial, seminar or recitation hours per week
      Lab = laboratory or field study hours per week
      C = number of course credits

For example, a course vector of [3-1-2:3] denotes a course that requires 3 lecture hours, 1 tutorial/seminar/recitation hour, and 2 laboratory/field study hours each week, and carries 3 credits.

Medium of Instruction

The medium of instruction is English.  Some courses will have the following notations in the course description to specify the language of reading materials or permitted spoken language (dialect) used in teaching.  

Postgraduate Grades

Students receive a grade in each course in which they are enrolled.  Grades range in equal increments from A+ to F (i.e. A+, A, A-, B+, B, B-, C+, C or F).   The Pass, Ungraded (P) grade is given only for courses that are indicated in the course description that they will be graded as such. 

CHEM 5210

Computational Chemistry

2-0-3:3

Fundamentals and applications of various computational chemistry methods, including molecular orbital calculations, molecular mechanics and molecular dynamics. Computational laboratory practice will be emphasized. Background: CHEM 3420

CHEM 5340

Chemical X-ray Crystallography

3-0-0:3

Applications of X-ray diffraction methods to the determination of crystal structures, including crystal symmetry, reciprocal lattice, intensity of diffraction, the phase problem, and refinement of structure parameters, powder X-ray diffraction analysis.

CIVL 5110

Engineering Risk, Reliability and Decision

3-0-0:3

Advanced reliability methods in engineering decision; Bayesian methods, system reliability and design, risk analysis, probabilistic observational method, Markov and availability models, random field, large-scale system simulation, decision with multiple objectives.
Exclusion(s): CIEM 5810
Prerequisite(s): CIVL 2160 or equivalent

CIVL 5210

Principles of Project Finance

3-0-0:3

In-depth discussion of principles, techniques, and models of project finance in capital-intensive infrastructure projects, including international infrastructure markets; project bankability; project agreement and ancillary contracts; risk analysis and management; financial structuring, modeling and evaluation; outsourcing; case studies of various public-private partnerships in infrastructure development.
Exclusion(s): CIEM 600F

CIVL 5340

Optimal Structural Design

3-0-0:3

Presents advanced theories for design optimization; linear and nonlinear mathematical programming techniques, approximation concepts, sensitivity analysis, optimality criteria method for large-scale structures, evolutionary optimization using genetic algorithms and simulated annealing.

CIVL 5430

Aquatic Chemistry

3-0-0:3

Chemistry applied to reactions occurring in water and wastewater, includes inorganic solution chemistry, chemical equilibrium, acids/bases, coordination chemistry, chemical kinetics, colloid chemistry, solubility and precipitation, oxidation-reduction potential.
Prerequisite(s): CIVL 2410

CIVL 5470

Industrial Wastewater Treatment

3-0-0:3

Procedures for industrial surveys; includes waste sampling, waste characterization, treatability studies, selection of treatment methods for achieving cost effective operation, case studies of selected types of industrial waste treatment.
Exclusion(s):JEVE 5470
Prerequisite(s): CIVL 3420

CIVL 5730

Theoretical and Computational Soil Mechanics

3-0-0:3

Advanced soil models and recent developments in numerical methods in geotechnical modeling, including constitutive laws, critical state soil mechanics, multiple yield surface models, finite elements for boundary value problems, diffusion and consolidation problems. Background: CIVL 3740

CIVL 5840

Advanced Concrete Technology

3-0-0:3

Fundamental concepts (workability, strength, dimension stability, and durability); updated concrete technology (micro structural engineering, development of special concretes); concrete fracture and modeling; nondestructive evaluation methods for concrete structures. Background: CIVL 2120 and CIVL 2810 or equivalent
Exclusion(s): CIEM 5240, CIVL 4810

CIVL 5850

Renovation Engineering

3-0-0:3

Reinforced concrete durability; damage caused by natural and human-being disaster; Infrastructure degradation, inspection; non-destructive evaluation; Conventional repair techniques; Composite materials; Steel plate or composite strengthening, beam and column retrofitting.
Exclusion(s): CIEM 5250

CIVL 6050X

Civil Engineering Seminar I

1-0-0:0

Discussion of current research by faculty members, and guest lectures on recent advances in civil engineering. Graded P or F.

CIVL 6100K

Hydroclimate Data Analysis and Modelling

3-0-0:3

This course provides a foundation for analytical methods used in hydroclimate studies. The course is module-based, with a designated suite of methods. Each module has a specific focus on the types of problems and/or datasets, which lead to a selection of methods and approach, to solve a series of progressive questions related to the module topics. Topics include hydroclimatic extremes, climate change and variability, from diagnosis to modelling for predictions. The course will introduce the open source statistical computing and graphic language, R. Methods include time series analysis, regressions, pattern recognition & dimension reduction. Students are expected to complete an individual project using R. Some multivariate statistical analysis method (e.g. factor analysis, classification & clustering) and advanced modeling methods (e.g. Bayesian hierarchical modeling) might be introduced, if they are suitable for the students’ projects. Instructor's approval is needed.

CIVL 6100M

Discrete Choice Experiments and Data Analysis

3-0-0:3

Discrete choice modeling framework and stated choice methods are widely applied across diverse fields to study the behavioral responses of individuals, households and organizations. This course is designed to provide both theory and practical experience in the building and estimating of advanced discrete choice models, as well as in generating stated choice experimental designs. This course covers both traditional discrete choice models and future developments in the field of discrete choice analysis including risk attitude and perceptual conditioning. This course also places the focus on generating stated choice surveys for answering real-world research questions and building models using real data. The techniques gained in this course are transferable to diverse areas of researching, such as transportation, logistics, health services, marketing, economics, tourism, planning. Instructor's approval is required.

COMP 5111

Fundamentals of Software Analysis

3-0-0:3

The goal of this course is to introduce how various analysis techniques can be used to manage the quality of a software application. Students will acquire fundamental knowledge of program abstraction, features, verification, testing, refactoring, concurrency, reliability, aspect orientation, and fault analysis. The course will also discuss how to carry out the empirical experimentation for program analysis. Wherever applicable, concepts will be complemented by tools developed in academia and industry. This enables students to understand the maturity and limitations of various analysis techniques.

COMP 5112

Parallel Programming

3-0-0:3

Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest. Background: COMP 3511 AND COMP 3711/COMP 3711H
Exclusion(s): COMP 6111B, COMP 6511A, COMP 6611A, MSBD 5009

COMP 5212

Machine Learning

3-0-0:3

Introduction to major learning paradigms and techniques, basic applied statistics and information theory, decision trees, neural networks, Bayesian classification, kernel methods, clustering, density estimation, feature selection and extraction, hidden Markov models, reinforcement learning, case-based learning, model selection and various applications. Background: COMP 2012, probability theory and linear algebra
Exclusion(s): CSIT 5910, MSBD 5012

COMP 5214

Advanced Deep Learning Architectures

3-0-0:3

[Co-list with ELEC 5680] [Previous Course Code: COMP 6211D] This course focuses on advanced deep learning architectures and their applications in various areas. Specifically, the topics include various deep neural network architectures with applications in computer vision, signal processing, graph analysis, and natural language processing. Different state-of-the-art neural network models will be introduced, including graph neural networks, normalizing flows, point cloud models, sparse convolutions,and neural architecture search. The students have the opportunities to implement deep learning models for some AI-related tasks such as visual perception, image processing and generation, graph processing, speech enhancement, sentiment classification, and novel view synthesis.
Exclusion(s): ELEC 5680

COMP 5311

Database Architecture and Implementation

3-0-0:3

Introduction to the relational model and SQL. System architectures and implementation techniques of database management systems: disk and memory management, access methods, implementation of relational operators, query processing and optimization, transaction management and recovery. Hands on experience with building the components of a small DBMS. Background: COMP 3511

COMP 5421

Computer Vision

3-0-0:3

Introduction to techniques for automatically describing visual data and tools for image analysis; perception of spatial organization; models of general purpose vision systems; computational and psychological models of perception. Background: COMP 3211; knowledge in linear algebra

COMP 5712

Introduction to Combinatorial Optimization

3-0-0:3

An introduction to the basic tools of combinatorial optimization, including network flow and the max-flow min-cut theorem, linear programming, matching, spanning trees and matroids, dynamic programming, algorithms and data structures, graph algorithms. Background: COMP 3711 or equivalent, linear algebra

COMP 5713

Computational Geometry

3-0-0:3

An introductory course in Computational Geometry. Algorithms for manipulating geometric objects. Topics include Convex Hulls, Voronoi Diagrams, Point Location, Triangulations, Randomized Algorithms, Point-Line Duality. Background: COMP 3711

COMP 6211G

Federated Learning

3-0-0:3

This course introduces the basic concepts, frameworks, applications and open problems in the emerging field federated learning. It includes discussions on important topics such as privacy, communication efficiency and incentives. Background: Required: Math, Statistics, Machine Learning (basics), Optional: Cryptography

COMP 6311E

High Dimensional Data Management and Analytics

3-0-0:3

In this course, we will introduce high dimensional data management concepts, theories and technologies, focusing on data access methods and similarity-based search for spatial, spatiotemporal and multimedia databases. Challenges and solution for high dimensional data, and current research problems and advances will also be introduced. Students are assumed to have knowledge about relational database systems. The minimum knowledge that students should have in order to take this course: good knowledge of SQL, database indexing techniques, DBMS architectures, query processing and optimisation.

COMP 6613B

Topics in Programming Languages: Semantics and Verification

3-0-0:3

This course provides an introduction to the three main approaches for defining the semantics of programming languages (operational, denotational, and axiomatic). It then illustrates how they can be utilized to prove basic program properties such as safety and termination. Finally, it looks into LTL verification from a logic-game-automaton perspective.

COMP 6613C

Topics in Computer Security and Privacy

3-0-0:3

This course is a broad post-graduate-level course that covers fundamental components and cutting-edge topics in computer security and privacy. The course consists of reading, presenting, and discussing published research papers. Students also need to complete an original research project individually or in a small group.

ELEC 5080

Integrated-Circuit Fabrication Laboratory

2-0-6:4

Laboratory course requiring hands-on work in fabricating MOS transistors. Process modules including photolithography, dry etching, wet etching, metal sputtering, oxidation, diffusion and low-pressure chemical-vapor deposition will be covered. Student will also learn to characterize the fabricated devices.
Prerequisite(s): ELEC 5070

ELEC 5140

Advanced Computer Architecture

3-0-0:3

[Previous Course Code: ELEC 6910K] The course introduces the important building blocks in modern computing systems including superscalar processor pipeline, memory hierarchies, network design in the multicore-processors. The design techniques, evaluation metrics and optimization techniques will be discussed in detail with the example of real computer systems. The students will gain not only theoretical knowledge through lectures, but also hands-on experiences through projects. Background: Background knowledge in ELEC 2300 (Computer Organization) or COMP 2611 (Computer Organization)

ELEC 5190

Solid State and Semiconductor Electronics

3-0-0:3

Crystal Lattices; lattice vibration and thermal properties of crystals; free-electron theory; electrons in periodic lattices; carrier transport; metal semiconductor contacts and semiconductor surfaces; optical processes. Background: ELEC 4510

ELEC 5210

Advanced Topics in Nanoelectronics

3-0-0:3

Introduction to state-of-the-art development in the broad area of nanoelectronics, including concepts and devices for spin electronics and quantum information science. Students are expected to demonstrate the capability of applying fundamental principles to understand advanced electronic devices through hands-on homework projects. Background: ELEC 4510

ELEC 5280

High Frequency Circuit Design

3-0-0:3

High frequency circuit design for wireless applications. S-parameters, front-end amp, VCO, PLL, power amplifier, and integration issues will be covered. Background: ELEC 3100, ELEC 3400, ELEC 4180 and ELEC 4630

ELEC 5360

Principles of Digital Communications

3-0-0:3

The aim of this course is to provide an in-depth treatment of the theoretical basis, analysis, and design of digital communication systems. The first half of the course will focus on the theoretical foundations of a basic digital communication system, including source coding, modulating and channel coding, and introductory information theory. The second half will deal with advanced techniques including orthogonal frequency division multiplexing (OFDM), multi-antenna communications, spread-spectrum communications, and cooperative communications. Background: Probability theory
Exclusion(s): EESM 5536

ELEC 5450

Random Matrix Theory and Applications

3-0-0:3

[Previous Course Code: ELEC 6910H] This course gives an introduction to random matrix theory (RMT), which has become a very important tool in communication systems, signal processing and a wealth of (high dimensional) statistical applications. Topics include: introduction to RMT models in engineering; eigenvalue distributions; Wishart and related distributions; finite-dimensional and large-dimensional techniques. Applications include wireless communications, array processing, robust covariance estimation, principal component analysis, signal detection, data analysis applications to financial and biomedical engineering. Background: UG-level probability (e.g., ELEC 2600 in ECE) is expected. No prior knowledge of wireless communications or signal processing is required

ELEC 5520

Power Management Integrated Circuit Design

3-0-0:3

Integrated circuit techniques for power management components such as voltage references, linear voltage regulators, low dropout regulators, switch mode power converters and switched-capacitor power converters. Background: ELEC 4420 and ELEC 4430

ELEC 5640

Robot Manipulation

3-0-0:3

[Co-list with MECH 5561] Extensive introduction to robot manipulation theory from a geometric viewpoint. Rigid-body kinematics; spatial and body representation of rigid-body velocities; coordinate transformations; forward kinematics of open-chain manipulators; solution of inverse kinematics; robot workspaces; nonlinear decoupling control and force control.
Exclusion(s): MECH 5561

ELEC 5650

Introduction to Networked Sensing, Estimation and Control

3-0-0:3

[Previous Course Code: ELEC 6910E] The course gives an introduction to the analysis and design of sensing, estimation and control systems in a networked setting. It consists of three parts: the first part introduces necessary background knowledge in communication networks, sensor networks, linear state estimation, MAP and ML estimators, Kalman filtering, and modern control theory; the second part focuses on analysis of network effect to remote state estimation and control; the third part presents some advanced topics including distributed state estimation and resource allocation through scheduling. Background: ELEC 2600 AND ELEC 3200

ELEC 5660

Introduction to Aerial Robotics

3-0-3:3

[Previous Course Code: ELEC 6910P] This course gives a comprehensive introduction to aerial robots. The goal of this course is to expose students to relevant mathematical foundations and algorithms, and train them to develop real-time software modules for aerial robotic systems. Topics to be covered include rigid-body dynamics, system modeling, control, trajectory planning, sensor fusion, and vision-based state estimation. Students will complete a series of projects which combine into an aerial robot that is capable of vision-based autonomous indoor navigation. Background: Linear algebra; Probability; MATLAB programming skills; C++ programming skills

ELEC 5680

Advanced Deep Learning Architectures

3-0-0:3

[Co-list with COMP 5214] [Previous Course Code: ELEC 6910T] This course focuses on advanced deep learning architectures and their applications in various areas. Specifically, the topics include various deep neural network architectures with applications in computer vision, signal processing, graph analysis, and natural language processing. Different state-of-the-art neural network models will be introduced, including graph neural networks, normalizing flows, point cloud models, sparse convolutions, and neural architecture search. The students have the opportunities to implement deep learning models for some AI-related tasks such as visual perception, image processing and generation, graph processing, speech enhancement, sentiment classification, and novel view synthesis.
Exclusion(s): COMP 5214

ELEC 5810

Introduction to Bioinformatics Algorithms

3-0-0:3

This is an introductory course on computational biology at the molecular level. It will cover basic biological knowledge, important biological questions, common data acquisition techniques, popular data analysis algorithms and their applications. The major content of this course is computation-oriented.  [BLD]
Exclusion(s): COMP 300G

ELEC 5820

Microfluidics and Biosensors

3-0-0:3

[Co-list with BIEN 5820] [Previous Course Code: ELEC 6910D] Introduction to Microfluidics and Biosensors; Overview of microfabrication materials & techniques; microfluidic principles; miniaturized biosensors; micro total analysis system (µTAS) & lab-on-a-chip (LOC) for clinical and research applications. Background: Basic Physics
Exclusion(s): BIEN 5820

ELEC 6910V

Advance Display Technologies

3-0-0:3

Introduction of the human visual system, Colorimetry and photometry, Introduction of the modern TFTs, Modern AMLCD, AMOLED, Fluorescence and phosphorescence, Introduction of Electrophoretic displays, Color electrophoretic displays, Nano-material for displays, Electroluminescence and Photoluminescence, Quantum dot, Quantum rods, State-of-the-art development in the area of display technology: High-resolution displays (4k, 8k, and 10k), Local backlight dimming, Introduction to AR/VR display solutions, Holographic displays, Flexible displays etc.

FINA 7900A

Doctoral Seminar: Theoretical Corporate Finance

3-0-0:3

The course will emphasize theoretical frameworks that underlie some of the important ideas in modern corporate finance. Topics covered will be (i) Financial Contracting, (ii) Capital Structure and Corporate Strategy , (iii) Agency Problems, Investment Distortions, and Discipline, (iv) Information Asymmetry and Financing, (v) Corporate Governance. The theory will be motivated by and connected to empirical facts, although we will not go into details of empirical papers on the topics. An important goal is to help students learn how to write down a basic model to capture an idea.
Exclusion(s): FINA 790I (prior to 2011-12)

FINA 7900B

Doctoral Seminar: Theoretical Asset Pricing

3-0-0:3

The course discusses fundamental theories in asset pricing.
Exclusion(s): FINA 790E (prior to 2011-12)

FINA 7900E

Doctoral Seminar: Continuous Time Finance

3-0-0:3

The course will cover most of the standard materials in continuous-time finance such as the Black-Scholes-Merton framework of derivatives pricing, Merton's dynamic programming approach and the Cox-Huang martingale approach of solving dynamic optimal portfolio choice problems, the term structure models of Vasicek, Cox, Ingersoll, and Ross (CIR), and Heath, Jarrow, and Morton (HJM), numerical methods for pricing derivatives and solving partial differential equations.
Exclusion(s): FINA 790D (prior to 2011-12)
Prerequisite(s): FINA 790E (prior to 2011-12); FINA 7900B (from 2011-12)

HUMA 5240

Chinese Dialectology

3-0-0:3

This course will provide an introductory survey of the phonology of Chinese dialects, including Mandarin, Wu, Xiang, Gan, Hakka, Yue and Min. [Pu][C]

HUMA 5440

Contemporary Chinese Fiction

3-0-0:3

A critical study of development, trends, characteristics of the Chinese fiction of the People's Republic from the early 1980s to the present. [Pu][C]

HUMA 5655

Contracts and Order in Chinese Local Society, 600-1911 AD

3-0-0:3

[Previous Course Code: HUMA 6002R] This course explores the role played by contracts and agreements in maintaining order in local society in pre-modern China, with an emphasis on demonstrating their value as sources for historical analysis. Main themes include use of contracts in daily life, categorization of contracts, functions of oral agreements and written contracts, settlement of disputes by customary law and civil litigation, power of local elites and the hierarchical background supporting contractual relations. Contracts and documents concern multi-ethnic areas in Southwest China, and students must possess the ability to read them in the original. Discussions will include not only deep reading of contracts, textual and historical analysis, but also their role as instruments of social control. This course is designed to guide students in creatively using contracts as sources for social history and is not a mere factual and descriptive account of Chinese contracts. [C]

HUMA 5690

Major Issues in the History of U.S.-China Relations

3-0-0:3

This course examines the historical origins and evolution of the complex relations between China and the United States from the early 19th century to the late 20th century. It explores some of the most important events and persistent issues in political, military, economic, and cultural relations between the two countries. It also introduces students to major competing interpretations by American and Chinese scholars. [C]

HUMA 5770

Field Research: Theory and Practice

3-0-0:3

[Previous Course Code: HUMA 5550] Theories, methods, and techniques in ethnographic field research are explored. Students conduct individual and group research projects.
Exclusion(s): MGCS 5031 (prior to 2018-19)

HUMA 6002T

Global Environmental History

3-0-0:3

Students will read books and articles about topics related to global environmental history, with a particular emphasis on recent environmental histories of East and Southeast Asia. Students will be expected to help lead class discussions, and students will produce a research paper as a major final project for the class.
Exclusion(s): MGCS 6000B

IEDA 5120

Revenue Management and Pricing Analytics

3-0-0:3

[Previous Course Code: IEDA 6100C] Ph.D.-level course covering current topics in Revenue Management and Pricing Analytics. The goal of the course is to provide students with the background and tools required to perform research in the field. The course is divided into two parts. Part 1 goes for ten weeks and consists of a combination of lectures on discrete-choice models, assortment optimization, revenue management with dependent demands, followed by lectures on pricing analytics including basic pricing theory, dynamic pricing and on-line learning. The lectures will be interspersed with paper presentations that reinforce the theory. Students are expected to read the material provided before coming to class. Part 2 will take place during the three last weeks of the course and will be devoted to project presentations. The instructor will provide a list of current research topics from which the students can select a class project, but students are free to propose their own projects.

IPEN 5100

Innovation, Policy and Entrepreneurship

3-0-0:3

This course focuses on the practices and processes that managers in the business sector adopt to advance innovation and attention is also paid to the strategies that policy-makers from regulatory background pursue to manage innovation. Technological innovation will be examined through its process of exploring, executing, leveraging, and renewing from both the perspectives of entrepreneurs and regulators. Students will be guided to seek a collaborative governance mechanism that is workable for different players and sectors in innovation to achieve sustainable growth.

LIFS 5260

Biochemical and Molecular Basis of Diseases

3-0-0:3

Advanced topics on the biochemical basis of human diseases, molecular medicine and structure based drug design; an oral presentation and a written essay on a specific topic are required. Background: LIFS 4760 or equivalent

MARK 5410

Seminar in Quantitative Modeling

3-0-0:3

Overview of the literature on modeling marketing phenomena.

MARK 5470

Seminar in Consumer Behavior II

3-0-0:3

Overview of advanced topics in psychology and consumer behavior research. An information processing approach is used to help students develop expertise on a range of diverse topics such as attitude formation and change, culture, information processing, motivation and goals, emotion, consumer decision making.

MATH 5112

Advanced Algebra II

3-0-0:3

Advanced topics in algebra: group representations, associative algebras, commutative algebra, homological algebra, algebraic number theory. Background: MATH 5111

MATH 5143

Introduction to Lie Algebras

3-0-0:3

Lie algebras. Nilpotent, solvable and semisimple Lie algebras. Universal enveloping algebras and PBW-theorem. Cartan subalgebras. Roots system, Weyl group, and Dynkin diagram. Classification of semisimple Lie algebras. Representations of semisimple algebras. Weyl character formula. Harish-Chandra isomorphism theorem.
Prerequisite(s): MATH 2131 and MATH 3131

MATH 5240

Algebraic Topology

3-0-0:3

Fundamental group, covering space, Van Kampen theorem, (relative) homology, exact sequences of homology, Mayer-Vietoris sequence, excision theorem, Betti numbers and Euler characteristic.

MATH 5261

Algebraic Geometry II

3-0-0:3

Derived functors, cohomology of coherent sheaves on schemes, extension groups of sheaves, higher direct image of sheaves, Serre duality, flat morphisms, smooth morphisms, and semi-continuity, basics of curves and surfaces. Background: MATH 5111 or equivalent postgraduate algebra
Prerequisite(s): MATH 5251

MATH 5281

Partial Differential Equations

3-0-0:3

[Previous Course Code: MATH 6050E] This is an introductory postgraduate course on Partial Differential Equations (PDEs). We will start with the classical prototype linear PDEs, and introduce a variety of tools and methods. Then we will extend our beginning theories to general situation using the notion of Sobolev spaces, Holder space and weak solutions. We will prove the existence, uniqueness, regularity and other properties of weak solutions. Background: Multi-variables calculus, linear algebra, Lebesgue integral

MATH 5312

Advanced Numerical Methods II

3-0-0:3

Direct and iterative methods. Programming techniques and softwares libraries. Sparse solvers, Fast algorithms, multi-grid and domain decomposition techniques.
Prerequisite(s): MATH 5311

MATH 5352

Mathematical Methods in Science and Engineering II

3-0-0:3

Asymptotic methods and perturbation theory for obtaining approximate analytical solutions to differential equations. Topics include: local analysis of solutions to differential equations, asymptotic expansion of integrals, global analysis and perturbation methods, WKB theory, multiple-scale analysis, homogenization method.
Prerequisite(s): MATH 5351

MATH 5412

Advanced Probability Theory II

3-0-0:3

Characteristic functions, limit theorems, law of the iterated logarithm, stopping times, conditional expectation and conditional independence, introduction to Martingales.

MATH 5450

Stochastic Processes

3-0-0:3

Theory of Markov processes, second order stationary theory, Poisson and point processes, Brownian motion, Martingales and queueing theory.

MATH 5470

Statistical Machine Learning

3-0-0:3

[Previous Course Code: MATH 6450A] This course covers methodology, major software tools and applications in statistical learning. By introducing principal ideas in statistical learning, the course will help students understand conceptual underpinnings of methods in data mining. The topics include regression, logistic regression, feature selection, model selection, basis expansions and regularization, model assessment and selection; additive models; graphical models, decision trees, boosting; support vector machines; clustering.
Exclusion(s):  MFIT 5010, MSDM 5054

MATH 5473

Topological and Geometric Data Reduction and Visualization

3-0-0:3

[Co-list with CSIC 5011] [Previous Course Code: MATH 6380Q] This course is a mathematical introduction to data analysis and visualization with a perspective of topology and geometry. Topics covered include: classical linear dimensionality reduction, the principal component analysis (PCA) and its dual multidimensional scaling (MDS), as well as extensions to manifold learning, topological data analysis, and sparse models in applied math/high dimensional statistics. Extensive application examples in biology, finance, and information technology are presented along with course projects.
Exclusion(s): CSIC 5011

MATH 6170D

An Introduction to Algebraic Number Theory

3-0-0:3

This is an introduction to the algebraic number theory. The topics to be covered include: number fields, algebraic integers, factorization, Dedekind domains, local rings, Units and class groups, Cyclotomic fields, p-adic numbers, Dedekind Zeta functions, Artin Zeta functions. Other topics (depending on interest: Weil explicit formula, number field and function field analogy, etc..)
Prerequisite(s): MATH 5111

MATH 6380T

Topics in Applied Dynamical Systems

3-0-0:3

Many problems in science and engineering are better understood as a dynamical system with internal mechanisms to evolve in time. This course illustrates how theories from dynamical systems can help analyze and interpret data with some recent developments. Particular emphasis will be given to the scenario where we have limited observations of a system consisting of a large number of variables, such as in the brain. We will first review classic stability and bifurcation theories and then discuss topics Including: theory and application of chaos, data-driven modeling and DMD, and fitting and Interpreting RNNs. The students will also have ample opportunity for hands-on practice of the discussed algorithms. The course is suited for both postgraduate and advanced undergraduate students. There is no formal prerequisite. However, the students are expected to be familiar with linear algebra, multivariable calculus, ordinary differential equations, basic probability, and statistics, and have experience in some programming language (e.g. python, MATLAB).

MECH 5230

Computational Fluid Dynamics and Heat Transfer

3-0-0:3

Numerical simulation of viscous incompressible flows and heat transfer; finite-difference and finite element methods; accuracy and stability; grid generation; stream function and primitive-variable formulations; application to internal, external flows, diffusion, convection, and dispersion problems. Background: Basic programming background (e.g. C/C++/Matlab)
Prerequisite(s): MECH 2210 or equivalent AND MECH 3310 or equivalent

MECH 5280

Transport Phenomena and Its Application in Energy Systems

3-0-0:3

[Co-list with ENEG 5400] Elementary statistical concepts; ensembles and postulates; partition functions and their properties; calculation of thermodynamic properties; kinetic theory of transport process; fluctuation-dissipation theorem; Langevin equation; mass and heat transfer in fuel cells. Exclusion(s): MECH 691Z, ENEG 5400

MECH 5320

Convective Heat and Mass Transfer

3-0-0:3

Laminar and turbulent boundary layer heat transfer by similarity, integral and superposition methods; effects of roughness, curvature, transpiration and high turbulence; forced and free convections, free-shear flows and buoyant flows; numerical methods. Background: MECH 3310
Prerequisite(s): MECH 5210

MECH 5561

Robot Manipulation

3-0-0:3

[Co-list with ELEC 5640] [Previous Course Code: MECH 6910M] Extensive introduction to robot manipulation theory from a geometric viewpoint. Rigid-body kinematics; spatial and body representation of rigid-body velocities; coordinate transformations; forward kinematics of open-chain manipulators; solution of inverse kinematics; robot workspaces; nonlinear decoupling control and force control.
Exclusion(s): ELEC 5640

MECH 5960

Flow Instability

3-0-0:3

[Previous Course Code: MECH 6910K] Capillary instabilities, centrifugal instabilities, shear instabilities, thermal-convective instabilities, normal mode decomposition, spatial vs. temporal analysis, linearization, nonlinear dynamics, routes to chaos, phase space reconstruction, transition to turbulence, and fluid-structure interactions.

MGMT 7150

Doctoral Seminar in Cognition

3-0-0:3

[Previous Course Code: MGMT 6510A] This is a research-oriented graduate level course on cognition. It introduces to doctoral students basic/philosophical and applied/practical topics on cognition. It offers the opportunity for students to integrate concepts in basic research on cognition with applied research in business. It offers the opportunity for students to understand scientific research methodology of studying cognition.

PHYS 5310

Statistical Mechanics I

3-0-0:3

Laws and applications of thermodynamics, kinetic theory, transport phenomena, classical statistical mechanics, canonical and grand canonical ensemble, quantum statistical mechanics, Fermi and Bose systems, non-equilibrium statistical mechanics.

PHYS 5370

Solid State Physics II

3-0-0:3

[Previous Course Code: PHYS 6810B] This is a second course on postgraduate level solid state physics. The thermal, electronic, magnetic and optical properties of solid will be studied. Semiconductor devices and electronics will be discussed. The theory of conventional and unconventional superconductors will be introduced. Special topics related to current research in solid state physics will be covered. These special topics include graphene, topological insulators, transition metal dichalcogenides and topological superconductors. Background: Students should have good understanding in undergraduate level quantum mechanics and undergraduate level solid state physics before taking this course.

PHYS 5810

Modern Semiconductor Physics

3-0-0:3

[Co-list with NANO 5200] Detailed explanations of the electronic, vibrational, transport, and optical properties of semiconductors based on quantum mechanics. Emphasis on nanostructured heterostructures, quantum size and low-dimensional effects, and application to modern electronics and opto-electronics. Background: PHYS 4052 or equivalent
Exclusion(s): NANO 5200

PPOL 5111

Foundation in Public Policy II

3-0-0:3

This course will introduce postgraduate students to advanced topics in public policy theory. It will build on PPOL 5110, with a greater focus on key topics including public management and administration; multilevel and network governance; policy evaluation; institutional analysis; and regulatory structures and implementation.
Prerequisite(s): PPOL 5110

PPOL 5120

Research Methods in Public Policy

3-0-0:3

[Co-list with SOSC 5790] This course is required for all first-year MPhil/PhD students in Public Policy. The purposes of the course are to introduce to students the key concepts in research methods, and to help them develop skills in the design of empirical research used in the analysis of policy problems. The course aims to train students to be able to apply various research designs in conducting rigorous policy research in their chosen fields, as well as develop the ability to critically evaluate policy research products. A specific emphasis will be on the use of quasi-experimental designs in policy research, as well as on their potentials and limitations.
Exclusion(s): SOSC 5790

SOSC 5170

Qualitative Research Methods

3-0-0:3

This course explores links between theory and practice in qualitative research. It combines learning about selected methods of qualitative inquiry (participant-observation, in-depth interview, oral history) and analysis (grounded theory, ethnography, and discourse analysis). Enrollment by students from outside the Division of Social Science by instructor permission. Background: Knowledge in Social Science
Prerequisite(s): SOSC 5110

SOSC 5680

Democracy and Democratization

3-0-0:3

Drawing from single-country and cross-national research, this course covers the following: i) basic features of democracy: its definitions, causes of emergence, strengths and problems; ii) global expansion of democracies since the late twentieth century; iii) research on whether democracy can promote human rights, whether there is a basic conflict between Asian values and democracy, and whether democracy is favourable or unfavourable to economic development; iv) causes of global democratization.

SOSC 5720

Economic Development in China

3-0-0:3

[Previous Course Code: SOSC 6030A] This course focuses on economic reforms and development in China, especially since 1978. It will be a combination of institutional details and comprehensive empirical evidence. Basic knowledge in statistics or economics will benefit.

SOSC 5790

Research Methods in Public Policy

3-0-0:3

[Co-list with PPOL 5120] The purposes of the course are to introduce to students the key concepts in research methods, and to help them develop skills in the design of empirical research used in the analysis of policy problems. The course aims to train students to be able to apply various research designs in conducting rigorous policy research in their chosen fields, as well as develop the ability to critically evaluate policy research products. A specific emphasis will be on the use of quasi-experimental designs in policy research, as well as on their potentials and limitations.
Exclusion(s): PPOL 5120

SOSC 6030K

Introduction to Social Network Analysis

1.5-1.5-0:3

This course familiarizes students with the basic concepts of Social Network Analysis, their application in different Social Science fields, and teaches them how to analyse network data using open-source software. The course consists both of lectures and applied exercises, the latter culminating in a group project. Knowledge of basic statistics is recommended, but not required for this course.

SOSC 6880

Seminar on Emotion

3-0-0:3

This course covers major perspectives on emotion, with an emphasis on a psychological rather than a biological or a sociological level of analysis. It provides an in-depth examination of emotion theories and research to students with different research foci. Background: This is a graduate level course designed for advanced and motivated students with background in (a) upper-level (non-1000 level) psychology courses and (b) research methods in psychological science.

UGOD 5010

Science of Cities

3-0-0:3

The course aims to provide a comprehensive understanding of the city and the system of cities, the challenges faced by cities, especially the rapidly-developing large cities, and the key tools for interventions in response to critical pressures linked to economic development, urbanization, globalization, migration, social inclusion, climate change, resource efficiency, technology etc.