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Week 5 Summary: Unsupervised Learning in Materials

Cross-Book Summary

1. The Need for Convolutions

Parameter Explosion: MLPs require too many weights for images.
Exploiting Structure: Convolutions use shared filters for spatial correlation.
Shift Invariance: Detects features anywhere in the micrograph.

2. CNN Mechanics

Convolutional Layers: Hierarchical feature detectors.
Activation & Pooling: Non-linearity and spatial downsampling.
Perception Analogy: Inspired by early photo-cell grids.

3. Application to Microstructures

Segmentation: Pixel-wise classification.
Object Detection: Bounding specific features.
Classification: Categorizing whole micrographs.

90-Minute Lecture Strategy

Part 1: Applied Clustering

Phase segmentation with K-Means/GMMs.
Defect detection in sensor streams.

Part 2: Applied Autoencoders

CAE compression of 3D Tomography.
Latent space defect discovery.

Quarto Website Update (Summary)

Summary for ML-PC Week 5:

Shifts to Unsupervised Learning for unlabeled materials data.
Covers clustering (K-Means, GMMs) and Autoencoders.
Applies techniques to EDS segmentation and sensor anomaly detection.
Explores 3D microstructure latent spaces for automated motif discovery.