# Lecture Plan: Unsupervised Learning in Materials (ML-PC Unit 05)

## Overview
This lecture applies the unsupervised methods learned in the mathematical foundations course directly to materials characterization, focusing on the "unlabeled data" problem in materials science.

## Part 1: Applied Clustering (45 mins)
*   **Case Study 1:** Automated phase segmentation in EDS/EDX maps using K-Means and GMMs. Grouping pixels into distinct chemical phases without manual labels.
*   **Case Study 2:** Anomaly and defect detection in acoustic or thermal sensor streams during manufacturing processes.

## Part 2: Applied Autoencoders (45 mins)
*   **Case Study 1:** Compressing high-dimensional microstructures. Using Convolutional Autoencoders (CAEs) to create a low-dimensional "materials latent space" from 3D X-ray Tomography volumes.
*   **Case Study 2:** Exploring the latent space. Clustering encoded latent vectors to automatically discover categories of defects or structural motifs without human supervision.