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Quarto GHA Workflow Runner · Quarto GHA Workflow Runner · commit d49f298e7a8a · 2026-05-09T18:32:23.000Z
diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-bd1bcd0f
+a4e1f103
diff --git a/index-meca.zip b/index-meca.zip
diff --git a/index-preview.html b/index-preview.html
@@ -271,13 +271,14 @@ <h2 id="toc-title">Table of contents</h2>
 
        <style>
 .course-hero {
-  background: rgba(20, 25, 40, 0.65);
-  border: 1px solid rgba(255,255,255,0.06);
-  border-radius: 18px;
-  padding: 1.5rem;
-  margin: 1rem 0 2rem 0;
-  backdrop-filter: blur(10px);
+  background: linear-gradient(135deg, rgba(59,130,246,0.18), rgba(139,92,246,0.14));
+  border: 1px solid rgba(255,255,255,0.08);
+  border-radius: 24px;
+  padding: 2.5rem 3rem;
+  margin: 2rem 0 3rem 0;
+  text-align: left;
 }
+.course-hero h1 { font-family: "Outfit", sans-serif; font-weight: 800; margin-bottom: 0.5rem; border: none; }
 .course-kicker {
   text-transform: uppercase;
   letter-spacing: 0.08em;
@@ -320,6 +321,7 @@ <h2 id="toc-title">Table of contents</h2>
   margin: 1.25rem 0 2rem 0;
 }
 @media (max-width: 768px) {
+  .course-hero { padding: 2rem; }
   .course-meta-grid { grid-template-columns: 1fr; }
 }
 </style>
diff --git a/index.docx b/index.docx
diff --git a/index.embed.ipynb b/index.embed.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "This course teaches how machine learning can be applied to experimental data from materials processing and characterization. The focus lies on images, spectra, time-series, and processing parameters, and on understanding how physical data formation interacts with learning algorithms. Students learn to build robust, uncertainty-aware ML pipelines for real experimental workflows, avoiding common pitfalls such as data leakage, overfitting, and spurious correlations."
    ],
-   "id": "a8ad62f3-a1a3-40a4-a2bd-69c5621870e5"
+   "id": "2a20b373-c106-4b1a-814f-363c665a41e3"
   },
   {
    "cell_type": "raw",
@@ -23,13 +23,14 @@
    "source": [
     "<style>\n",
     ".course-hero {\n",
-    "  background: rgba(20, 25, 40, 0.65);\n",
-    "  border: 1px solid rgba(255,255,255,0.06);\n",
-    "  border-radius: 18px;\n",
-    "  padding: 1.5rem;\n",
-    "  margin: 1rem 0 2rem 0;\n",
-    "  backdrop-filter: blur(10px);\n",
+    "  background: linear-gradient(135deg, rgba(59,130,246,0.18), rgba(139,92,246,0.14));\n",
+    "  border: 1px solid rgba(255,255,255,0.08);\n",
+    "  border-radius: 24px;\n",
+    "  padding: 2.5rem 3rem;\n",
+    "  margin: 2rem 0 3rem 0;\n",
+    "  text-align: left;\n",
     "}\n",
+    ".course-hero h1 { font-family: \"Outfit\", sans-serif; font-weight: 800; margin-bottom: 0.5rem; border: none; }\n",
     ".course-kicker {\n",
     "  text-transform: uppercase;\n",
     "  letter-spacing: 0.08em;\n",
@@ -72,11 +73,12 @@
     "  margin: 1.25rem 0 2rem 0;\n",
     "}\n",
     "@media (max-width: 768px) {\n",
+    "  .course-hero { padding: 2rem; }\n",
     "  .course-meta-grid { grid-template-columns: 1fr; }\n",
     "}\n",
     "</style>"
    ],
-   "id": "b6ecbb1f-4985-43ac-9878-eec986728040"
+   "id": "ce3ac6e6-3e5f-4e93-912a-677a788c4150"
   },
   {
    "cell_type": "raw",
@@ -106,7 +108,7 @@
     "  <strong>How to use this course site.</strong> Use this page as the central hub for syllabus, lecture structure, reading, notebooks, and course materials. Formal announcements and enrollment remain on StudOn; code and openly shared resources live in the linked GitHub repository.\n",
     "</div>"
    ],
-   "id": "708cc477-6fae-4cb2-b3a7-a38ae9efd9f0"
+   "id": "77f5543f-6fac-403f-86a9-d1de908e46b3"
   },
   {
    "cell_type": "markdown",
@@ -448,7 +450,7 @@
     "- ML-assisted autofocus or EBSD pattern classification.\n",
     "- Multi-modal fusion of images, spectra, and process parameters."
    ],
-   "id": "a91aef87-eeae-423b-bc0b-3706f0befa72"
+   "id": "dd8795b9-3baa-454b-980e-166bc44d9f8c"
   }
  ],
  "nbformat": 4,
diff --git a/index.html b/index.html
@@ -211,13 +211,14 @@ <h2 id="toc-title">Table of contents</h2>
 
 <style>
 .course-hero {
-  background: rgba(20, 25, 40, 0.65);
-  border: 1px solid rgba(255,255,255,0.06);
-  border-radius: 18px;
-  padding: 1.5rem;
-  margin: 1rem 0 2rem 0;
-  backdrop-filter: blur(10px);
+  background: linear-gradient(135deg, rgba(59,130,246,0.18), rgba(139,92,246,0.14));
+  border: 1px solid rgba(255,255,255,0.08);
+  border-radius: 24px;
+  padding: 2.5rem 3rem;
+  margin: 2rem 0 3rem 0;
+  text-align: left;
 }
+.course-hero h1 { font-family: "Outfit", sans-serif; font-weight: 800; margin-bottom: 0.5rem; border: none; }
 .course-kicker {
   text-transform: uppercase;
   letter-spacing: 0.08em;
@@ -260,6 +261,7 @@ <h2 id="toc-title">Table of contents</h2>
   margin: 1.25rem 0 2rem 0;
 }
 @media (max-width: 768px) {
+  .course-hero { padding: 2rem; }
   .course-meta-grid { grid-template-columns: 1fr; }
 }
 </style>
diff --git a/index.out.ipynb b/index.out.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "This course teaches how machine learning can be applied to experimental data from materials processing and characterization. The focus lies on images, spectra, time-series, and processing parameters, and on understanding how physical data formation interacts with learning algorithms. Students learn to build robust, uncertainty-aware ML pipelines for real experimental workflows, avoiding common pitfalls such as data leakage, overfitting, and spurious correlations."
    ],
-   "id": "bd82efaa-db8b-44b1-97c3-6787fd4a3edb"
+   "id": "2dead55d-282e-4189-8c3a-6fb15c7dfcdc"
   },
   {
    "cell_type": "raw",
@@ -23,13 +23,14 @@
    "source": [
     "<style>\n",
     ".course-hero {\n",
-    "  background: rgba(20, 25, 40, 0.65);\n",
-    "  border: 1px solid rgba(255,255,255,0.06);\n",
-    "  border-radius: 18px;\n",
-    "  padding: 1.5rem;\n",
-    "  margin: 1rem 0 2rem 0;\n",
-    "  backdrop-filter: blur(10px);\n",
+    "  background: linear-gradient(135deg, rgba(59,130,246,0.18), rgba(139,92,246,0.14));\n",
+    "  border: 1px solid rgba(255,255,255,0.08);\n",
+    "  border-radius: 24px;\n",
+    "  padding: 2.5rem 3rem;\n",
+    "  margin: 2rem 0 3rem 0;\n",
+    "  text-align: left;\n",
     "}\n",
+    ".course-hero h1 { font-family: \"Outfit\", sans-serif; font-weight: 800; margin-bottom: 0.5rem; border: none; }\n",
     ".course-kicker {\n",
     "  text-transform: uppercase;\n",
     "  letter-spacing: 0.08em;\n",
@@ -72,11 +73,12 @@
     "  margin: 1.25rem 0 2rem 0;\n",
     "}\n",
     "@media (max-width: 768px) {\n",
+    "  .course-hero { padding: 2rem; }\n",
     "  .course-meta-grid { grid-template-columns: 1fr; }\n",
     "}\n",
     "</style>"
    ],
-   "id": "9382e95c-8cd8-467a-b410-3ff94506065f"
+   "id": "f42c5ac0-4033-4cfe-be11-d516fc45100f"
   },
   {
    "cell_type": "raw",
@@ -106,7 +108,7 @@
     "  <strong>How to use this course site.</strong> Use this page as the central hub for syllabus, lecture structure, reading, notebooks, and course materials. Formal announcements and enrollment remain on StudOn; code and openly shared resources live in the linked GitHub repository.\n",
     "</div>"
    ],
-   "id": "eb97ea47-42b5-45bb-ab4c-56e3ef06909a"
+   "id": "9066c33e-3d8a-427d-8f67-962690f83423"
   },
   {
    "cell_type": "markdown",
@@ -450,7 +452,7 @@
     "\n",
     "Sandfeld, Stefan. 2024. *Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering*. Springer Nature."
    ],
-   "id": "5a6b7251-c41c-4f4c-98f0-8cedad87043c"
+   "id": "de61ab95-f281-4d00-a3a1-a3df3a317f28"
   }
  ],
  "nbformat": 4,
diff --git a/index.pdf b/index.pdf
diff --git a/index.qmd b/index.qmd
@@ -52,13 +52,14 @@ execute:
 ```{=html}
 <style>
 .course-hero {
-  background: rgba(20, 25, 40, 0.65);
-  border: 1px solid rgba(255,255,255,0.06);
-  border-radius: 18px;
-  padding: 1.5rem;
-  margin: 1rem 0 2rem 0;
-  backdrop-filter: blur(10px);
+  background: linear-gradient(135deg, rgba(59,130,246,0.18), rgba(139,92,246,0.14));
+  border: 1px solid rgba(255,255,255,0.08);
+  border-radius: 24px;
+  padding: 2.5rem 3rem;
+  margin: 2rem 0 3rem 0;
+  text-align: left;
 }
+.course-hero h1 { font-family: "Outfit", sans-serif; font-weight: 800; margin-bottom: 0.5rem; border: none; }
 .course-kicker {
   text-transform: uppercase;
   letter-spacing: 0.08em;
@@ -101,6 +102,7 @@ execute:
   margin: 1.25rem 0 2rem 0;
 }
 @media (max-width: 768px) {
+  .course-hero { padding: 2rem; }
   .course-meta-grid { grid-template-columns: 1fr; }
 }
 </style>
diff --git a/unit04_microstructure_representations_gemini.out.ipynb b/unit04_microstructure_representations_gemini.out.ipynb
@@ -68,7 +68,7 @@
     "- **Slide 50: Future Outlook & Questions**\n",
     "  - Open challenges (data scarcity, physics-informed neural networks)."
    ],
-   "id": "e76e9850-4bb1-4901-9871-a5f4632a2a9c"
+   "id": "6dd4b978-3e8f-480e-bf63-d8d0a6f4fb91"
   }
  ],
  "nbformat": 4,
diff --git a/unit05_plan_gemini.out.ipynb b/unit05_plan_gemini.out.ipynb
@@ -22,7 +22,7 @@
     "- **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.\n",
     "- **Case Study 2:** Exploring the latent space. Clustering encoded latent vectors to automatically discover categories of defects or structural motifs without human supervision."
    ],
-   "id": "daa902f3-34d3-44e2-b19c-1f068396b59c"
+   "id": "9b30cab1-f1fa-4478-be9e-7039f8b93084"
   }
  ],
  "nbformat": 4,
diff --git a/week10_summary.out.ipynb b/week10_summary.out.ipynb
@@ -63,7 +63,7 @@
     "**Summary for ML-PC Week 10:**  \n",
     "- Processes high-dimensional Characterization Signals (XRD, EDS). - Employs K-Means and t-SNE for automated phase identification. - Uses Autoencoders for latent space compression and denoising. - Enhances high-throughput data analysis while preserving physics."
    ],
-   "id": "c4a793a2-cdbc-442c-944b-73ba48cd4cab"
+   "id": "aff558ef-5601-4dae-8ecd-9e460ee76f43"
   }
  ],
  "nbformat": 4,
diff --git a/week11_summary.out.ipynb b/week11_summary.out.ipynb
@@ -60,7 +60,7 @@
     "**Summary for ML-PC Week 11:**  \n",
     "- Explores Autonomous Characterization and active instrument control. - Introduces Multi-Modal Data Fusion (Bayesian and Latent). - Uses Reinforcement Learning (RL) for laboratory task automation. - Details building integrated pipelines for “on-the-fly” scientific discovery."
    ],
-   "id": "7deece1c-1efe-45b2-9deb-54bb84668929"
+   "id": "72d755b8-85a0-4410-a2d5-cf6c2fb02af2"
   }
  ],
  "nbformat": 4,
diff --git a/week12_summary.out.ipynb b/week12_summary.out.ipynb
@@ -60,7 +60,7 @@
     "**Summary for ML-PC Week 12:**  \n",
     "- Introduces Probabilistic Machine Learning for uncertainty quantification. - Differentiates aleatoric (noise) from epistemic (ignorance) uncertainty. - Uses Gaussian Processes (GPs) for uncertainty-aware regression. - Applies confidence intervals to map robust process windows."
    ],
-   "id": "da6eb6de-e28c-4e4f-a1eb-c88ebfb9069f"
+   "id": "abaf504a-6243-4778-9460-6cbb4fb32c5f"
   }
  ],
  "nbformat": 4,
diff --git a/week13_summary.out.ipynb b/week13_summary.out.ipynb
@@ -62,7 +62,7 @@
     "**Summary for ML-PC Week 13:**  \n",
     "- Combines neural networks with physical laws via Physics-Informed ML. - Introduces PINNs and automatic differentiation. - Details Governing Equation Discovery using sparse regression. - Applies physical constraints to build data-efficient Grey-Box models."
    ],
-   "id": "e4bd3bf8-860f-4f15-af02-0f0cf21366aa"
+   "id": "7a0fb0d9-704e-4423-ba72-00b5c12fca5d"
   }
  ],
  "nbformat": 4,
diff --git a/week14_summary.out.ipynb b/week14_summary.out.ipynb
@@ -62,7 +62,7 @@
     "**Summary for ML-PC Week 14:**  \n",
     "- Reflects on Explainability and Sensitivity Analysis to open black-box models. - Discusses Causality in process chains and Ontologies for semantic reasoning. - Critically assesses Data Bias and physical AI hallucinations. - Examines the evolving partnership between autonomous algorithms and human experts."
    ],
-   "id": "39d4211f-63b1-45d8-92bb-237974410591"
+   "id": "019c08b6-2669-4af6-b24d-6082f630c38d"
   }
  ],
  "nbformat": 4,
diff --git a/week1_summary.out.ipynb b/week1_summary.out.ipynb
@@ -66,7 +66,7 @@
     "**Summary for ML-PC Week 1:**  \n",
     "- Transitions from physics-based to data-driven modeling. - Highlights multi-modal, scarce materials data. - Covers PSPP relationships, data scales, and uncertainty. - Adapts CRISP-DM for scientific workflows."
    ],
-   "id": "d085100c-7f21-4644-b27a-902f8b441cd6"
+   "id": "65578857-c075-4f0f-ab54-140f83099241"
   }
  ],
  "nbformat": 4,
diff --git a/week2_summary.out.ipynb b/week2_summary.out.ipynb
@@ -61,7 +61,7 @@
     "**Summary for ML-PC Week 2:**  \n",
     "- Connects physical data acquisition to mathematical representation. - Analyzes signal formation, resolution, and noise as priors. - Introduces PCA and SVD for low-dimensional structure discovery. - Demonstrates dimensionality reduction in high-dimensional datasets."
    ],
-   "id": "3bdb96f5-1c93-4d9b-8089-81b0609f8830"
+   "id": "83b15420-93c6-4af2-9096-234a0ecd0d1f"
   }
  ],
  "nbformat": 4,
diff --git a/week3_summary.out.ipynb b/week3_summary.out.ipynb
@@ -63,7 +63,7 @@
     "**Summary for ML-PC Week 3:**  \n",
     "- Emphasizes data integrity, cleaning, and normalization. - Highlights materials annotation challenges like inter-annotator variance. - Details Data Leakage risks from physical/spatial correlations. - Introduces robust validation to ensure true generalization."
    ],
-   "id": "cf4208c0-703a-44d4-9545-69764a0b4b58"
+   "id": "def3ab2d-b4c3-44e4-9d03-084ea3a215d6"
   }
  ],
  "nbformat": 4,
diff --git a/week4_summary.out.ipynb b/week4_summary.out.ipynb
@@ -60,7 +60,7 @@
     "**Summary for ML-PC Week 4:**  \n",
     "- Transitions from classical stereology to learned representations. - Reviews limits of hand-crafted microstructure metrics. - Introduces the artificial neuron, weights, and activations. - Builds the framework for Multi-Layer Perceptrons (MLPs) to automate feature extraction."
    ],
-   "id": "bd8914ab-4ec7-4e34-bb0f-98346bad8c10"
+   "id": "5273d6f7-2835-4fd2-80ed-20b0b0ef0edd"
   }
  ],
  "nbformat": 4,
diff --git a/week5_summary.out.ipynb b/week5_summary.out.ipynb
@@ -44,7 +44,7 @@
     "\n",
     "**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."
    ],
-   "id": "a25fb7e0-80de-42c1-8c87-3705122e6dfd"
+   "id": "382d4d7a-d2ba-411f-838e-137fda807ec1"
   }
  ],
  "nbformat": 4,
diff --git a/week6_summary.out.ipynb b/week6_summary.out.ipynb
@@ -62,7 +62,7 @@
     "**Summary for ML-PC Week 6:**  \n",
     "- Addresses Data Scarcity in materials informatics. - Explores Transfer Learning to leverage pretrained models. - Discusses scientific Data Augmentation strategies. - Evaluates cross-domain knowledge transfer limits."
    ],
-   "id": "7f417446-710e-4295-9f20-60f9dd496357"
+   "id": "1125112c-ff77-47bc-a926-df0ff1f0b078"
   }
  ],
  "nbformat": 4,
diff --git a/week7_summary.out.ipynb b/week7_summary.out.ipynb
@@ -60,7 +60,7 @@
     "**Summary for ML-PC Week 7:**  \n",
     "- Applies ML to Time-Series Data for process monitoring. - Introduces RNNs and LSTMs for sequential dependencies. - Details essential preprocessing like smoothing and triggering. - Covers anomaly detection and process outcome prediction."
    ],
-   "id": "2882a756-f79c-4d03-8e03-44888bcde44c"
+   "id": "3d41db2f-2dd2-4637-9654-94d6ea9f2e9e"
   }
  ],
  "nbformat": 4,
diff --git a/week8_summary.out.ipynb b/week8_summary.out.ipynb
@@ -63,7 +63,7 @@
     "**Summary for ML-PC Week 8:**  \n",
     "- Shifts focus from performance to Model Reliability. - Explores Bias-Variance tradeoff and generalization. - Details robust validation (K-Fold, Stratified CV). - Uses sensitivity analysis to map stable Process Windows."
    ],
-   "id": "f172c13c-5b60-41c2-b724-d63a696eea62"
+   "id": "53803d97-039e-4a82-b805-78d07f824750"
   }
  ],
  "nbformat": 4,
diff --git a/week9_summary.out.ipynb b/week9_summary.out.ipynb
@@ -63,7 +63,7 @@
     "**Summary for ML-PC Week 9:**  \n",
     "- Explores Inverse Problems for materials design. - Contrasts multi-valued inverse tasks with causal forward problems. - Introduces Physics-Informed Learning and feature enrichment. - Demonstrates building Process Maps and Safe Corridors."
    ],
-   "id": "aec48fa2-f15c-478a-b9dd-378f686ee2d6"
+   "id": "43ca6804-3262-4a7d-b6d0-648a3ebd1679"
   }
  ],
  "nbformat": 4,

Original file line number	Diff line number	Diff line change
`@@ -68,7 +68,7 @@`
`68`	`68`	`"- Slide 50: Future Outlook & Questions\n",`
`69`	`69`	`" - Open challenges (data scarcity, physics-informed neural networks)."`
`70`	`70`	`],`
`71`		`- "id": "e76e9850-4bb1-4901-9871-a5f4632a2a9c"`
	`71`	`+ "id": "6dd4b978-3e8f-480e-bf63-d8d0a6f4fb91"`
`72`	`72`	`}`
`73`	`73`	`],`
`74`	`74`	`"nbformat": 4,`