Postgraduate Courses
IEDA
Industrial Engineering and Decision Analytics
- IEDA 5120Revenue Management and Pricing Analytics[3-0-0:3]Previous Course Code(s)IEDA 6100CDescriptionPh.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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand elements of Discrete Choice Models.
- 2.Understand the concepts of Assortment optimization.
- 3.Understand the theory of Revenue management.
- 4.Understand the principles of Overbooking.
- 5.Understand the concepts of Basic Pricing Theory.
- 6.Understand the principles of Dynamic Pricing.
- 7.Understand the concepts of Oligopoly.
- 8.Understand On-line learning algorithms.
- IEDA 5170Production and Operations Management[3-0-0:3]Previous Course Code(s)IELM 5170Exclusion(s)ISOM 5700DescriptionThe course introduces concepts, principals and techniques related to the design, planning, management and improvement of both manufacturing and service operations. Topics include demand forecasting and estimation, inventory management, production control and process improvement, queueing systems, procurement and supply chain management.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Understand the basic concepts, principles and approaches in production and operations management.
- 2.Understand different functions including demand forecasting, aggregate planning, inventory management, production control and scheduling in a production system.
- 3.Identity problems and opportunities for the improvement of a production system.
- 4.Apply knowledge of production management concepts and functions to evaluate and improve the performance of an integrated operation system.
- 5.Apply qualitative frameworks and quantitative models to a firm’s production management problem.
- IEDA 5230Deterministic Models in Operations Research[3-0-0:3]Previous Course Code(s)IELM 5230Prerequisite(s)IEDA 3010DescriptionThis course focuses on the theory and the use of deterministic optimization models for real life decision making problems. It covers linear, integer, combinatorial and nonlinear programming.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Master the fundamental theories of Linear and nonlinear programs.
- 2.Apply the theories to model and analyze problems arising in practice.
- 3.Solve mathematical program problems.
- 4.Be familiar with some of the applications of mathemtical programs.
- IEDA 5250Stochastic Models in Operations Research[3-0-0:3]Previous Course Code(s)IELM 5250BackgroundMATH 2411DescriptionPoisson processes, renewal processes, Markov processes. Fundamental concepts and applications of these stochastic processes demonstrated through examples in queueing, inventory and reliability models.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Master the fundamental theories of Poisson processes, renewal processes, discrete-time Markov chains, and continuous-time Markov chains.
- 2.Apply various stochastic processes to developing stochastic models for practical problems.
- 3.Analyze stochastic models to study related quantities of interest.
- 4.Solve practical problems mathematically via stochastic modelling.
- 5.Develop stochastic models for solving problems in operations research.
- 6.Analyze and solve stochastic models used in operations research.
- IEDA 5270Engineering Statistics and Data Analytics[3-0-0:3]Previous Course Code(s)IELM 5270DescriptionThe course introduces advanced concepts and mathematical principles in statistical inference (e.g., estimation theory, hypothesis testing, and regression models) and machine learning (e.g. classification and tree-based models, support vector machines, model selection, and unsupervised learning). This course assumes the knowledge of multivariable calculus and probability.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain the mathematical principles of statistical inference and machine learning.
- 2.Lay the foundation for advanced statistical courses, such as machine learning and Bayesian statistics.
- 3.Conduct empirical data analysis for academic research.
- 4.Identify and implement cutting-edge statistical learning models to projects from the industry.
- IEDA 5470Convex Optimization[3-0-0:3]Previous Course Code(s)IEDA 6100ACo-list withELEC 5470Exclusion(s)ELEC 5470BackgroundStudents are expected to have a solid background in math and linear algebra, and good experience in reading scientific papersDescriptionConvex optimization theory with applications in signal processing, finance, and machine learning. It covers fundamentals (convex sets/functions/problems, Lagrange duality, algorithms), more advanced optimization techniques (sparsity, low-rank, robust optimization, decomposition methods, distributed algorithms), and specific applications (e.g., portfolio optimization, filter/beamforming design, classification methods, wireless communication systems, circuit design, image processing, data-drived graph learning, discrete MLE, network optimization, Internet protocol design, etc.).For PG students in second year or above.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Learn the basic theory of convex optimization.
- 2.Read a paper and understand the optimization component in a critical way.
- 3.Learn fundamental optimization techniques such as majorization-minimization method, robust optimization, sparse optimization, low-rank optimization, SDP relaxation, and decomposition methods.
- 4.Learn specific applications such as low-rank optimization for matrix completion, portfolio optimization, sparse regression in index tracking, SDP relaxation for maximum likelihood detection, graph learning from
data, Internet as a convex optimization problem, support vector machine for classification, etc. - 5.Deal with a new problem using the learned optimization tools.
- IEDA 6000Special Topics in Manufacturing Systems[1-3 credit(s)]Previous Course Code(s)IELM 6000DescriptionSelected topics of current interest. May be repeated for credit if different topics covered.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Stay abreast of contemporary issues on Manufacturing Systems.
- IEDA 6100Special Topics in Systems Engineering/Operation Research[1-3 credit(s)]Previous Course Code(s)IELM 6100DescriptionSelected topics of current interest. May be repeated for credit if different topics covered.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Stay abreast of contemporary issues in Industrial Engineering and Decision Analytics.
- IEDA 6300Special Topics in Transportation Logistics Management[1-3 credit(s)]Previous Course Code(s)IELM 6300DescriptionSelected topics of current interest. May be repeated for credit if different topics covered.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Stay abreast of contemporary issues on Transportation Logistics Management.
- IEDA 6800Departmental Seminar[1-0-0:0]Previous Course Code(s)IELM 6800DescriptionSeries of seminars by faculty and guest speakers, repeated every term. Research postgraduate students are expected to attend regularly and register for at least two terms. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Stay abreast of contemporary issues.
- 2.Recognize the need for, and to engage in life-long learning.
- 3.Understand professional and ethical responsibility.
- IEDA 6850Advanced Seminar[2-0-0:0]Previous Course Code(s)IELM 6850DescriptionAn in-depth study of a current research area in Industrial Engineering and Decision Analytics. Offerings are announced each term. Graded P or F.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Stay abreast of contemporary issues.
- 2.Recognize the need for, and to engage in life-long learning.
- 3.Understand professional and ethical responsibility.
- IEDA 6900Research Project[1-3 credit(s)]Previous Course Code(s)IELM 6900DescriptionAn independent research project carried out under the supervision of a faculty member. This course is only available for exchange, visiting and visiting internship students.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Adopt a self-learning approach to solve problems.
- 2.Identify the criteria of the selected research topic/area.
- 3.Recognise relevant research questions and apply appropriate methods and tools to effectively find solutions.
- IEDA 6950Independent Study[1-3 credit(s)]Previous Course Code(s)IELM 6950DescriptionSelected topics in industrial engineering and decision analytics studied under the supervision of a faculty member. Graded P or F. (Only one independent study course may be used to satisfy the course requirements for any postgraduate program in the Department of Industrial Engineering and Decision Analytics.)Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Adopt a self-learning approach to solve problems.
- 2.Identify the criteria of the selected research topic/area.
- IEDA 6990MPhil Thesis ResearchPrevious Course Code(s)IELM 6990DescriptionMaster's thesis research supervised by a faculty member. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply theory to research problems.
- 2.Adopt a self-learning approach to solve problems.
- 3.Demonstrate an ethical, responsible, and dependable attitude.
- 4.Use problem solving strategies to think critically and creatively in Industrial Engineering and Decision Analytics problems.
- 5.Communicate the problem, solution and its application in form of written and oral reports.
- 6.Recognise relevant research questions and apply appropriate methods and tools to effectively find solutions.
- IEDA 7990Doctoral Thesis ResearchPrevious Course Code(s)IELM 7990DescriptionOriginal and independent doctoral thesis research. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Apply theory to research problems.
- 2.Adopt a self-learning approach to solve problems.
- 3.Demonstrate an ethical, responsible, and dependable attitude.
- 4.Use problem solving strategies to think critically and creatively in Industrial Engineering and Decision Analytics problems.
- 5.Communicate the problem, solution and its application in form of written and oral reports.
- 6.Recognise relevant research questions and apply appropriate methods and tools to effectively find solutions.
