COURSES

「Graduate School」

[INE9102 - Stochastic Process]

  • Description: This lecture focuses on studying the basics of stochastic process. Topics covered during the class are the probability theory, Markov chain, renewal process, martingale, and Brownian motion.

  • Semester: Fall (Biennial)


[INE6055 - Financial Engineering & Risk Management]

  • Description: This lecture focuses on acquiring mathematical background, related theories, and risk management methods using financial engineering based on knowledge of probability theory and stochastic processes. In the first half, we will learn the concept of various types of options with related mathematical models and risk management methods using the Greeks. In the second half, we will learn numerical methods for valuation(pricing) and risk measures to broaden the knowledge in theories and applications of financial engineering. This course is a follow-up to the Introduction to Financial Engineering, and focuses on developing in-depth knowledge of financial engineering theories and risk management.

  • Semester: Fall (Biennial)

  • Pre-req: INE3083(Intro. to Financial Engineering)


「INE6056 - Advanced Time-series Analysis & Forecasting」

  • Description: This lecture focuses on acquiring theories and mathematical models for analyzing and predicting time-series based data. In the first half, we will learn the basics of time series analysis and the stationary/non-stationary models. In the second half, we will learn advanced time series analysis and forecasting methods. This lecture aims at graduate-level time series course covering basic concepts to the latest trends, and focuses on developing relevant knowledge for students to utilize in setting further research topics and methods.

  • Semester: Spring (Biennial)



「Graduate School of Engineering」

[INF0011 - Decision Theory]

  • Description: This lecture focuses on studying the basics of decision theory. Topics covered during the class are the decision tree, expected utility, net present value, and real options valuation.

  • Semester: Fall (Biennial)


[INF9024 - Time-series Analysis]

  • Description: This lecture focuses on extracting meaningful information by applying various time-series models to real-world data. In the first half, we will learn the basics of time series analysis, and then explore smoothing- and decomposition-based models. In the second half, we will learn model identification, estimation, and diagnostic processes, and then realize the stationary and non-stationary time-series models. This lecture is an introductory course for the time-series analysis where the primary goal is fostering the ability to apply models to real data through R programming.

  • Semester: Fall (Biennial)



「Undergraduate」

[INF2076 - Investment Science]

  • Description: This lecture focuses on acquiring and analyzing various investment-related decisions that engineers will face from a mathematical perspective. In the first half, we will study the scientific methods for investing in projects within enterprises. Then, in the second half, we will learn the quantitative investment methods in financial markets. Through the course, students are expected to comprehend the applications of mathematical models and the basis of evidence-based decision-making in investment.

  • Semester & Target: Fall / Sophomore (English-medium Instruction)

  • Pre-req: GEN2053(Calculus 2), MAT2017(Probability & Statistics)


[INE3098 - Time-series Analysis & Forecasting]

  • Description: This lecture focuses on acquiring various techniques and methods used to utilize time-series data. In the first half, we will learn the basics of time series analysis, and then explore smoothing-based, decomposition-based, and stationary time series models. In the second half, we will learn model identification, estimation, and diagnostic processes, and then study the non-stationary, seasonal ARIMA, and models with heteroscedasticity. This lecture is an introductory course for the time-series analysis and forecasting where the primary goals are understanding theories with minimal proofs and fostering the ability to apply them to real data through R programming.

  • Semester & Target: Spring / Junior (English-medium Instruction)

  • Pre-req: GEN2053(Calculus 2), MAT2017(Probability & Statistics), COE3003(Applied Statistics)


[INE3083 - Introduction to Financial Engineering]

  • Description: This lecture focuses on cultivating the ability to understand and implement the basic theories of financial engineering based on quantitative knowledge in mathematics. In the first half, we will learn theories in the stock and bond markets based on the basic knowledge regarding the financial markets. In the second half, we will learn the basic theory of futures and options and related mathematical models. This lecture is an introductory course to financial engineering where the primary goals are developing basic knowledge about the financial markets, products, the uses of derivatives, and calculating their fair values.

  • Semester & Target: Spring / Senior (English-medium Instruction)

  • Pre-req: GEN2053(Calculus 2), MAT2017(Probability & Statistics), INE3080(MSOR 2), and intermediate-level programming ability in one of the following language (Matlab, Python, R, C, JAVA, or VBA)


[INE4108- Applied Data Analytics]

  • Description: This lecture focuses on developing the ability to broadly understand the concepts in data analytics and their applications across industries. It is a problem-based learning class; thus, we will learn through team discussions and researches on major algorithms and application methods in weekly basis. Finally, each team will select the most interesting topic of the semester, set it as a team project, and use the real-world data to conduct team-level exercises and final presentation.

  • Semester & Target: Fall / Senior

  • Pre-req: MAT2017(Probability & Statistics), INE5008(Data Mining), and intermediate-level programming ability in one of the following language (Matlab, Python, R, C, JAVA, or VBA)