Teaching

This page documents provide useful information for writing or revising academic papers, coding, and keeping updated with AI/ML news. It's especially beneficial for those studying remote sensing and hydrology.

All
GIST Courses
Coding
ML/ETC
Deep Learning Applications in Environmental Big Data Course at GIST (Fall 2024) EN5425/EV4240
This course offers an introduction to deep learning techniques for analyzing environmental big data. It covers basic concepts and progresses to advanced architectures like Convolutional (CNNs) and Recurrent Neural Networks (RNNs). Students will learn to collect, preprocess, and analyze large-scale environmental datasets, including remote sensing and hydrological data. Key topics include flood prediction, drought assessment, wildfire detection, and the use of global satellite images with land surface models for real-world applications. Emphasis is placed on integrating remote sensing with hydrological models for water resource management. By course end, students will produce a manuscript applying deep learning to environmental challenges.
Enter
Water Quality Assessment Using NASA Satellite Images and Machine Learning Over the Korean Peninsula (2024 광주과고-GIST 창의연구 프로그램)
In this workshop, you will learn to assess water quality using advanced remote sensing techniques and machine learning models, focusing on the Korean Peninsula. Leveraging NASA Sentinel-2 satellite data, we will explore the principles of remote sensing, data preprocessing, and feature extraction. You will gain hands-on experience in applying machine learning algorithms to analyze and predict water quality parameters such as Chlorophyll-a concentration. By the end of the workshop, you will be equipped with the skills to utilize satellite imagery and machine learning for environmental monitoring and water quality management.
Enter
Data Analysis and Statistical Inference Course at GIST (Spring 2024) EN5423
This course is designed to simplify the complex world of data analysis. It starts by teaching the basics of statistical inference, making smart guesses and decisions based on data. It then shifts to big data analysis, exploring how to handle, analyze, and draw conclusions from large datasets using modern techniques and tools. This course is ideal for anyone looking to make informed decisions using data, blending foundational statistical concepts with practical big data applications in a clear, straightforward manner.
Enter
Applied Machine Learning for Environmental Data Analysis Course at GIST (Fall 2023) EN5422/EV4238
This course delves into machine learning and data mining techniques tailored for environmental challenges, using data from ground observations, satellites, and models. Covering both supervised and unsupervised methods, students will learn to analyze and interpret complex environmental data through hands-on exercises and projects. By the end, participants will possess the skills to apply these techniques to real-world environmental issues and contribute to resource preservation.
Enter
Optimizing Python Code: A Case Study with the Bisect Algorithm
In the world of programming, one of the most common scenarios we encounter is having to optimize code that's running too slowly. This can be particularly challenging when working with large datasets or complex algorithms. Today, we're going to look at a practical example of how to optimize Python code to make it run faster.
Enter
Understanding and Addressing Causal Language in Research Writing
Causal Language' in Scientific Research: A Guide for Graduate Students.
Enter
Deep Learning Applications in Environmental Big Data Course at GIST (Fall 2024) EN5425/EV4240
This course offers an introduction to deep learning techniques for analyzing environmental big data. It covers basic concepts and progresses to advanced architectures like Convolutional (CNNs) and Recurrent Neural Networks (RNNs). Students will learn to collect, preprocess, and analyze large-scale environmental datasets, including remote sensing and hydrological data. Key topics include flood prediction, drought assessment, wildfire detection, and the use of global satellite images with land surface models for real-world applications. Emphasis is placed on integrating remote sensing with hydrological models for water resource management. By course end, students will produce a manuscript applying deep learning to environmental challenges.
Enter
Water Quality Assessment Using NASA Satellite Images and Machine Learning Over the Korean Peninsula (2024 광주과고-GIST 창의연구 프로그램)
In this workshop, you will learn to assess water quality using advanced remote sensing techniques and machine learning models, focusing on the Korean Peninsula. Leveraging NASA Sentinel-2 satellite data, we will explore the principles of remote sensing, data preprocessing, and feature extraction. You will gain hands-on experience in applying machine learning algorithms to analyze and predict water quality parameters such as Chlorophyll-a concentration. By the end of the workshop, you will be equipped with the skills to utilize satellite imagery and machine learning for environmental monitoring and water quality management.
Enter
Data Analysis and Statistical Inference Course at GIST (Spring 2024) EN5423
This course is designed to simplify the complex world of data analysis. It starts by teaching the basics of statistical inference, making smart guesses and decisions based on data. It then shifts to big data analysis, exploring how to handle, analyze, and draw conclusions from large datasets using modern techniques and tools. This course is ideal for anyone looking to make informed decisions using data, blending foundational statistical concepts with practical big data applications in a clear, straightforward manner.
Enter
Applied Machine Learning for Environmental Data Analysis Course at GIST (Fall 2023) EN5422/EV4238
This course delves into machine learning and data mining techniques tailored for environmental challenges, using data from ground observations, satellites, and models. Covering both supervised and unsupervised methods, students will learn to analyze and interpret complex environmental data through hands-on exercises and projects. By the end, participants will possess the skills to apply these techniques to real-world environmental issues and contribute to resource preservation.
Enter
Optimizing Python Code: A Case Study with the Bisect Algorithm
In the world of programming, one of the most common scenarios we encounter is having to optimize code that's running too slowly. This can be particularly challenging when working with large datasets or complex algorithms. Today, we're going to look at a practical example of how to optimize Python code to make it run faster.
Enter
Deep Learning Applications in Environmental Big Data Course at GIST (Fall 2024) EN5425/EV4240
This course offers an introduction to deep learning techniques for analyzing environmental big data. It covers basic concepts and progresses to advanced architectures like Convolutional (CNNs) and Recurrent Neural Networks (RNNs). Students will learn to collect, preprocess, and analyze large-scale environmental datasets, including remote sensing and hydrological data. Key topics include flood prediction, drought assessment, wildfire detection, and the use of global satellite images with land surface models for real-world applications. Emphasis is placed on integrating remote sensing with hydrological models for water resource management. By course end, students will produce a manuscript applying deep learning to environmental challenges.
Enter
Water Quality Assessment Using NASA Satellite Images and Machine Learning Over the Korean Peninsula (2024 광주과고-GIST 창의연구 프로그램)
In this workshop, you will learn to assess water quality using advanced remote sensing techniques and machine learning models, focusing on the Korean Peninsula. Leveraging NASA Sentinel-2 satellite data, we will explore the principles of remote sensing, data preprocessing, and feature extraction. You will gain hands-on experience in applying machine learning algorithms to analyze and predict water quality parameters such as Chlorophyll-a concentration. By the end of the workshop, you will be equipped with the skills to utilize satellite imagery and machine learning for environmental monitoring and water quality management.
Enter
Understanding and Addressing Causal Language in Research Writing
Causal Language' in Scientific Research: A Guide for Graduate Students.
Enter