Schedule
Course Outline
This course introduces statistical and machine learning methods for circular economy. Students will build Python skills using Jupyter Lab and Colab while working through weekly coding notebooks and graded assignments. Core topics include resampling methods (bootstrap, cross-validation), penalized regression, risk modeling, classification, density estimation, tree-based models, and ensemble methods. Readings are drawn from An Introduction to Statistical Learning and The Elements of Statistical Learning. A midterm project proposal, final presentation, and report provide hands-on experience applying these methods to real-world data. By the end, students will be prepared to analyze and interpret complex time-series, spatial, and spatio-temporal datasets with both statistical rigor and modern machine learning approaches.
Prerequisites
• Knowledge in linear regression analysis, statistical inference, and linear algebra.
• Basic working knowledge in a scientific programming language (e.g., Python, R, etc).
• All course examples will be in Python.
Textbooks and References
• Introduction to Linear Regression Analysis, Fifth Edition by Montgomery, Peck, and Vining. ISBN: 978-0-470-54281-1
• An Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Hastie, Tibshirani, and Friedman
• Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
• This course requires the use of the following statistical and typesetting software:
• Anaconda
• Additional course material and reading assignments will be provided via instructor notes and recent journal articles.
Grading
• Attendance: 10%
• Assignments: 50%
• Project Proposal: 15%
• Final Project Report: 25%
Additional Information
• Course materials will be available on the class webpage.
• Pre-class assignments (reading and coding) are required to prepare for the lectures. Failure to complete these may result in a lower participation grade.
Contact
For more information or inquiries about the course, please contact Prof. Hyunglok Kim at hyunglokkim@gist.ac.kr.