Undergraduate

Operations Research is a quantitative approach to decision making based on the scientific method of problem solving. This course is an introduction to the key aspects of Operations Research methodology. Students will learn how to model and solve a variety of deterministic problems using optimization techniques. Topics will include basic theory, model formulation, solution techniques, and result analysis/interpretation.

The aim of this course is to understand how to conduct statistical tests, analysis, and inference using Python programming language. At the end of the course, students will be able to construct efficient algorithms for statistical procedures without relying on built-in functions.

Operations Management is concerned with the advancement of production and delivery of goods and services in industries. In this course, we will learn and apply concepts and methods in Operations Management, including forecasting, optimal production and delivery, quality engineering, supply chain management, and product/service system design.

This course gives you understanding of fundamental concepts, techniques, and algorithms in data mining, beginning with topics such as linear regression, classification and clustering, and ending up with association rule mining and recommender systems. Students are strongly encouraged to identify and solve real-world industrial problems using data mining techniques.

Operations Research II is the second part of a two-course sequence of Operations Research that develops/analyzes models commonly used in the analysis of complex decision-making problems. This course will extend the course materials discussed in Operations Research I and will introduce students to several important types of mathematical and stochastic (probabilistic) models and solution techniques, including dynamic programming, stochastic processes, queueing models, inventory control, supply chain management, and revenue management.

Service systems in transportation, retail, healthcare, entertainment, hospitality, and other areas are configurations of people, information, organizations, and technologies that operate together for specific functions and values. The field of Service Science is emerging for the study of complex service systems, and involves methods and theories from a range of disciplines, including operations, industrial engineering, marketing, computer science, psychology, information systems, design, and more. In this course, we will learn and apply concepts and methods in Service Science for service management and engineering. In particular, we will focus on the application of artificial intelligence to service management and engineering.

This course introduces the basics of modern time series analysis. Students will learn about the characteristics of time series data and the basics of time series regression and exploratory data analysis. Then, we will cover various models and techniques in time series analysis including ARMA/ARIMA models, spectral analysis and filtering, and state space models. In addition, some additional topics including GARCH models or artificial neural network (ANN) models would be briefly introduced if time allows. The analyses will be performed using Python.

This course introduces the basic knowledge on various financial instruments as well as quantitative models for finance. The main topics include: equities, fixed-income securities, derivatives including options and futures, asset pricing models, and investment management.

Technology management is a set of management disciplines that allows organizations to manage their technological fundamentals to create competitive advantage. This course will cover a variety of topics and quantitative methods in the field of technology management. Students are expected to learn the ways of integrating data science into different types of problems in the field of technology management.

The objective of this course is to teach various methods that can be used for improving the quality of products and processes. Topics for this course are quality system requirements, designed experiments, process capability analysis, measurement capability, statistical process control, and acceptance sampling plans.

Business processes are ubiquitous in modern organizations and their execution is increasingly supported by advanced information systems, which make available a large amount data related to their design and execution. The first part of this course focuses on the typical phases of business process management in an organisation, that is, business process identification, business process modelling (using BPMN 2.0), and business process analysis and improvement. The second part focuses on process mining, that is, a state of the art technique to extract knowledge about business processes, e.g., process models, from the logs of the IT systems supporting their execution.

This is an undergraduate level course in applied machine learning, which is designed for juniors or seniors. The primary emphasis will be learning how machine learning algorithms can be applied to solve complex real-world problems. At the end of the course, students will be able to (1) learn about basic and advanced machine learning, including deep learning, (2) identify various real-world problems for the use of machine learning, and (3) employ machine learning algorithms to solve the real-world problems in various fields.

The 21st century has been the golden era of machine learning, and the GPU-based deep learning algorithms becomes an indispensable tool in both science and engineering. This course introduces the fundamentals of deep learning and its applications to image and sequential data. The objective of this course is to help students to understand the elements of deep learning, from the basic operations to the advanced architectures in the recent AI research, in the view of statistics and computer science. Topics for the course include convolutional networks, recurrent neural networks, and attention mechanisms.

Financial planning of individuals or institutions involves identifying investors’ financial goals and liabilities, providing optimal investment and consumption plans, and overseeing the actual implementation of financial plans. In this course, we study the process of financial planning, relevant theories including modern portfolio theory and asset-liability management, and relevant optimization and machine learning techniques. In addition, we will learn how to implement various financial planning problems using Python.

This course introduces blockchain technology. The objective of this course is to cover the basics of blockchain technology as a technology for designing and implementing cross-organisational information systems. The course starts with an overview of blockchain technology and its emergence in the field of cryptocurrency and then will focus more extensively on designing systems using blockchain. The course will look both at applications of blockchain in real world scenarios and at the more technical aspects related with the implementation of such systems.

This is an undergraduate level course in social network analysis, which is designed for juniors or seniors. This course introduces students to the basic concepts and analysis techniques in (online) social network analysis. Students will develop modeling and analysis skills using online user-generated data, especially in social network analysis. At the end of the course, students will be able to (1) learn the fundamentals of network theory, (2) understand the basic concepts of social network analysis, (3) analyze large-scale online user-generated data on social networks (e.g., social media, such as Facebook or Twitter), and (4) apply machine learning techniques to discover knowledge from online social networks.

Students and strategic partners from industry will work in project teams and undertake management engineering industrial projects. The teams must aim to disseminate completed project outcomes to industry. The progress of each project will be reviewed based on formal presentations

This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.

This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.

This course is designed to discuss contemporary topics in Management Engineering. Actual topics and cases will be selected by the instructor and may vary from term to term.