Junghyun (Andy) Kim

​Last updated on January 1, 2026

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Introduction to Machine Learning

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This course provides basic engineering mathematics required for understanding machine learning algorithms and a broad introduction to machine learning techniques with an emphasis on addressing problems arising in computational science and engineering. In addition to introducing the machine learning algorithms, this course features individual and group projects that help students apply the algorithms to real-world problems in preparation for both industry jobs and advanced courses in graduate programs. Learn more about this course through the YouTube video clip!​

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• Offered in Spring 2026 and Spring 2025 for undergraduate students at Sejong University

• Offered in Summer 2024 and Spring 2024 for undergraduate students at Seoul National University

• Offered in Spring 2024, Fall 2023, Spring 2023, and Fall 2022 for undergraduate students at Handong Global University

• Offered in Spring 2024, Fall 2023, and Spring 2023 for distance learning students via the UNESCO UNITWIN program

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Algorithm

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This course introduces students to designing algorithms with an emphasis on addressing problems arising in computational science and engineering. In addition to introducing various algorithm design methodologies, this course features individual and group projects that allow students to put into practice algorithm design techniques for solving real-world problems in preparation for both industry jobs and advanced courses in graduate programs. The goal of this course is to provide a solid background in designing algorithms in computing applications and expose students to algorithm design methodologies used in practice for learning how to implement algorithm strategies to real-world problems.

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• Offered in Spring 2024, Spring 2023, and Spring 2022 for undergraduate students at Handong Global University

• Offered in Spring 2024 for distance learning students via the UNESCO UNITWIN program

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Optimization for Engineering Design

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This course provides an introduction to optimization and related methods for engineering design. More specifically, it offers unconstrained optimization, constrained optimization, metaheuristic optimization, multi-objective optimization, surrogate-based optimization, and graph-based path optimization. This course features individual and team projects aimed at assisting students in preparing for industry jobs. 

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• Offered in Spring 2025 for graduate students at Sejong University

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Introduction to Aerospace Engineering

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This course offers an introduction to the field of Aerospace Engineering (AE), serving as the first taste of AE for those interested in the aerospace domain. Emphasis is placed on the fundamental concepts of AE with a focus on key topics: Aerodynamics, Performance, Propulsion, Structure, and Stability & Control. In addition to covering basic principles, this course also provides topics related to Aerospace Systems Engineering and Systems Design & Optimization for understanding a system-level approach to designing and optimizing complex aerospace systems. In a nutshell, the main objective of this course is to expose students to AE material across all main areas. 

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• Offered in Spring 2025 for undergraduate students at Sejong University

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Introduction to Engineering Design

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Engineering design is not carried out in a completely free design space. In practice, it must satisfy customer needs, conform to the principles of physics, deal with various uncertainties, and meet the limits of time and budget. Successful engineering design relies on systematic thinking. In this course, engineering design is treated in a holistic process, starting from requirement analysis, design space exploration, optimization, robust design, and informed decision-making. Students will learn a systematic approach to design that is built on both practical design experiences and decision-making principles. There are several secondary objectives for this course, which include learning research methodologies, developing presentation skills, enhancing technical writing skills, and improving collaborative teamwork skills.

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• Offered in Fall 2025 for undergraduate students at Sejong University

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Hand-written notes

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Machine Learning​ ​​

• Random Forest

• Adaptive Boosting

• Sampling Methods

• K-Nearest Neighbor

• Hidden Markov Model

• Support Vector Machine

• Artificial Neural Network

• Expectation-Maximization 

• Markov Chain Monte Carlo

• Multi-Layer Perceptron with Backpropagation

• Density-Based Spatial Clustering of Application with Noise

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Optimization ​​

• KKT Conditions

• Indirect Method​

• Runge-Kutta Method

• Optimality Conditions

• Transportation Problem

• Unconstrained Optimization

• Particle Swarm Optimization

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Algorithm ​​​

• Genetic Algorithm

• Asymptotic Notation

• A* Search Algorithm

• Simulated Annealing Algorithm

• Traveling Salesman Problem

• Non-deterministic Polynomial Problems

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Statistics​​

• Box Plot

• Kernel Density Estimation

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Viscous Flow ​

• Viscosity

• Flow Separation

• Reynolds Analogy

• Navier-Stokes Equations

• Boundary Layer Equation 

• Stability of Laminar Flow

• Vorticity Transport Equation

• Compressible Boundary Layer

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Aerodynamics ​​

• Continuum

• Drag Polar

• Perfect Gas

• Panel Method

• Kutta Conditions

• Kelvin's Theorem

• High Lift Devices

• Thin Airfoil Theory

• Pressure Coefficient

• Bernoulli's Equation

• Aerodynamic Heating

• Full Potential Equation

• Aerodynamic Efficiency

• Transonic Aerodynamics

• Delta Wing Aerodynamics

• Hypersonic Aerodynamics

• Prandtl's Lifting Line Theory

• Supersonic Thin Airfoil Theory

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Aircraft Design ​

• Structure

• Master Equation

• Mission Analysis

• Constraint Analysis 

• Equations of Motion

• Air-Breathing Engines

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Lecture slides​

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To be updated