Applied Machine Learning for Environmental Data Analysis Course at GIST (Fall 2023) EN5422/EV4238


Course Outline

This course provides a comprehensive exploration of machine learning and data mining methods applied to real-world environmental problems. We will leverage data from ground observations, Earth observing satellites, and hydrological models to gain insights and address pressing environmental challenges.

Machine learning encompasses a diverse set of techniques that enable computers to learn patterns and make predictions or decisions without being explicitly programmed. Throughout the course, students will delve into the fundamental principles and practical applications of machine learning in conjunction with data mining, specifically tailored to environmental contexts. Data mining, as an integral part of the course, involves the extraction of valuable insights and knowledge from large volumes of environmental data. By employing various algorithms and statistical techniques, we can uncover hidden patterns, correlations, and trends that are crucial for understanding and mitigating environmental issues.

The course places a strong emphasis on the balanced coverage of supervised and unsupervised machine learning methods within the environmental domain. Supervised learning techniques will enable students to train models using labeled environmental data to make predictions or classifications. In contrast, unsupervised learning techniques will focus on discovering patterns and structures in unlabeled environmental data. By exploring both approaches, students will develop a comprehensive understanding of how machine learning can be harnessed to address a wide range of environmental challenges.

Hands-on exercises, projects, and case studies will provide students with practical experience in implementing machine learning and data mining algorithms using real-world environmental datasets. By leveraging data from ground observations, Earth observing satellites, and hydrological models, students will gain the skills necessary to effectively analyze and interpret complex environmental data, extract valuable insights, and make data-driven decisions to support environmental sustainability.

By the end of this course, students will have acquired a solid foundation in machine learning and data mining techniques, specifically tailored to address environmental problems. They will be equipped with the necessary skills to leverage diverse environmental datasets and models, contributing to the effective management and preservation of our natural resources.


Students taking this course should have prior knowledge in linear regression analysis, statistical inference, and linear algebra.Students should also have a basic working knowledge in  a scientific program language (e.g., Python, Matlab, R, etc). All course examples will be in Python.


출석 (10%), 퀴즈 (20%), 과제 (50%), 프로젝트 (20%)