Public Presentations of the Pelz Lab

Central link index for lecture decks and units.

Data Science for Electron Microscopy

• Unit 01: Intro — Slides · Source
• Unit 02: Regression — Slides · Source
• Unit 03: CNNs — Slides · Source
• Unit 04: Self-Supervised Learning — Slides · Source
• Unit 05: GANs — Slides · Source
• Unit 06: Gaussian Processes — Slides · Source
• Unit 07: Gaussian Processes II — Slides · Source
• Unit 08: Imaging Inverse Problems I — Slides · Source
• Unit 09: Imaging Inverse Problems II — Slides · Source

Mathematical Foundations of AI & ML

• Unit 01: What Learning Means in Engineering and Materials — Slides · Source
• Unit 02: Linear Algebra for Machine Learning — Slides · Source
• Unit 03: Regression and Classification as Loss Minimization — Slides · Source
• Unit 04: Neural Network Architectures and Activations — Slides · Source
• Unit 05: Backpropagation and Gradient Flow — Slides · Source
• Unit 06: Loss Landscapes and Optimization Behavior — Slides · Source
• Unit 07: Generalization, Bias-Variance, and Regularization — Slides · Source
• Unit 08: The Probabilistic View of Learning — Slides · Source
• Unit 09: Representation Learning — Slides · Source
• Unit 10: Latent Spaces and Embeddings — Slides · Source
• Unit 11: Unsupervised Learning — Slides · Source
• Unit 12: Uncertainty in Predictions — Slides · Source
• Unit 13: Physics-Informed Learning — Slides · Source
• Unit 14: Explainability, Limits, and Trust — Slides · Source

Materials Genomics

• Unit 01: What is Materials Genomics? — Slides · Source
• Unit 02: Simulation Methods as Data Generators — Slides · Source
• Unit 03: Atomistic and Electronic Simulations — Slides · Source
• Unit 04: Continuum Simulations, Thermodynamics, and Stability — Slides · Source
• Unit 05: Graph-Based Crystal Representations — Slides · Source
• Unit 06: Local Atomic Environments — Slides · Source
• Unit 07: Regression and Generalization in Materials Data — Slides · Source
• Unit 08: Neural Networks for Materials Properties — Slides · Source
• Unit 09: Representation Learning and Feature Discovery — Slides · Source
• Unit 10: Latent Spaces of Materials — Slides · Source
• Unit 11: Clustering vs Discovery in Materials Spaces — Slides · Source
• Unit 12: Uncertainty-Aware Discovery & Gaussian Processes — Slides · Source
• Unit 13: Physical Constraints, Trust, and Integration Outlook — Slides · Source

Machine Learning for Characterization and Processing

• Unit 01: Intro — Slides · Source
• Unit 02: Image Formation and Physics of Data — Slides · Source
• Unit 03: Experimental Data Quality and ML Readiness — Slides · Source
• Unit 04: Classical ML for Characterization Tasks — Slides · Source
• Unit 05: Deep Learning for Microscopy and Spectroscopy — Slides · Source
• Unit 06: Segmentation, Detection, and Feature Extraction — Slides · Source
• Unit 07: Process–Structure–Property Modeling — Slides · Source
• Unit 08: Surrogate Models for Process Optimization — Slides · Source
• Unit 09: Physics-Informed ML in Processing — Slides · Source
• Unit 10: Real-Time/Edge ML in Experiments — Slides · Source
• Unit 11: Explainability and Uncertainty in Lab Decisions — Slides · Source
• Unit 12: Closed-Loop Experiment Control — Slides · Source
• Unit 13: End-to-End Case Study (Data to Decision) — Slides · Source

Conference Talks

• 2025 MC — Slides · Source
• 2025 M&M — Slides · Source

Notes

• Slides links point to the published site path on pelzlab.science.
• Source links point to editable Quarto source files in this repository.
• Materials Genomics Unit 3–13 were realigned to match MaterialsGenomics/index.qmd; see materials_genomics/REALIGNMENT_OLD_TO_NEW_MAPPING.md.