Week 1: Overview of Applied AI
Lecture
Status: Lecture notes and slides are available.
Overview
The opening lecture builds the big picture of applied artificial intelligence: what AI, machine learning, and deep learning are, how real problems become AI tasks, and why a model only creates value inside a working system. Two published case studies from engineering practice (partial inverse design of a glass run channel, and deep-learning reconstruction of missing petroleum well-log data) show what applied AI looks like end to end. Course logistics (schedule, grading, and the semester project) are presented separately in class.
Materials
| Material | Where |
|---|---|
| Lecture notes | read |
| Slides | open (view in browser) |
| Lab | - |
| Papers | - |
References
[1] A. Nugraha, H. Kwon, G. Park, J. Lee, “Cooperative neural networks for inverse design: Integrating denoising autoencoder and surrogate model for partial design variable imputation,” Engineering Applications of Artificial Intelligence 162 (2025) 112453. Read the paper.
[2] G. Antariksa, R. Muammar, A. Nugraha, J. Lee, “Deep sequence model-based approach to well log data imputation and petrophysical analysis: A case study on the West Natuna Basin, Indonesia,” Journal of Applied Geophysics 218 (2023) 105213. Read the paper.