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index-meca.zip

@@ -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."
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     "  <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>"
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@@ -379,7 +379,7 @@
     "\n",
     "**Summary:** This unit explores the cutting edge of **Autonomous Characterization**, where machine learning moves from passive data analysis to active instrument control. We introduce **Multi-Modal Data Fusion** techniques to combine information from diverse sensors like SEM images, EDS spectra, and process logs using Bayesian frameworks. We then discuss **Reinforcement Learning (RL)** as a tool for automating complex laboratory tasks, such as instrument tuning and process optimization. Through case studies in microscopy and industrial processing, students learn how to build integrated pipelines that can autonomously find, characterize, and decide the next steps of an experiment."
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index.docx

@@ -169,7 +169,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#lab-possibilities" id="toc-lab-possibilities" class="nav-link" data-scroll-target="#lab-possibilities"><span class="header-section-number">1.5</span> Lab Possibilities</a></li>
   </ul></li>
   </ul>
-</nav>
+<div class="quarto-alternate-notebooks"><h2>Notebooks</h2><ul><li><a href="week1_summary-preview.html"><i class="bi bi-journal-code"></i>Week 1 Summary: What makes materials data special?</a></li><li><a href="week10_summary-preview.html"><i class="bi bi-journal-code"></i>Week 10 Summary: ML for characterization signals</a></li><li><a href="week11_summary-preview.html"><i class="bi bi-journal-code"></i>Week 11 Summary: Automation in microscopy and characterization</a></li><li><a href="week12_summary-preview.html"><i class="bi bi-journal-code"></i>Week 12 Summary: Uncertainty-aware regression &amp; Gaussian Processes</a></li><li><a href="week13_summary-preview.html"><i class="bi bi-journal-code"></i>Week 13 Summary: Physics-informed and constrained ML</a></li><li><a href="week14_summary-preview.html"><i class="bi bi-journal-code"></i>Week 14 Summary: Integration, limits, and reflection</a></li><li><a href="week2_summary-preview.html"><i class="bi bi-journal-code"></i>Week 2 Summary: Physics of data formation</a></li><li><a href="week3_summary-preview.html"><i class="bi bi-journal-code"></i>Week 3 Summary: Data quality, labels, and leakage</a></li><li><a href="week4_summary-preview.html"><i class="bi bi-journal-code"></i>Week 4 Summary: From classical microstructure metrics to learned representations</a></li><li><a href="week5_summary-preview.html"><i class="bi bi-journal-code"></i>Week 5 Summary: Neural networks for microstructure images</a></li><li><a href="week6_summary-preview.html"><i class="bi bi-journal-code"></i>Week 6 Summary: Data scarcity &amp; transfer learning</a></li><li><a href="week7_summary-preview.html"><i class="bi bi-journal-code"></i>Week 7 Summary: Time-series and process monitoring</a></li><li><a href="week8_summary-preview.html"><i class="bi bi-journal-code"></i>Week 8 Summary: Generalization, robustness, and process windows</a></li><li><a href="week9_summary-preview.html"><i class="bi bi-journal-code"></i>Week 9 Summary: Inverse problems and process maps</a></li></ul></div></nav>
 </div>
 <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
 </div>

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."
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@@ -106,7 +106,7 @@
     "  <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>"
    ],
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@@ -381,7 +381,7 @@
     "\n",
     "Sandfeld, Stefan. 2024. *Materials Data Science: Introduction to Data Mining, Machine Learning, and Data-Driven Predictions for Materials Science and Engineering*. Springer Nature."
    ],
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