Junghyun (Andy) Kim

​Last updated on December 31, 2024 

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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 2024 and Summer 2024 for students at Seoul National University

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

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

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

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Lecture slides

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To be updated

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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