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-f495d5b8
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index-meca.zip

@@ -6,7 +6,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
     <meta name="author" content="Philipp Pelz">
-    <meta name="dcterms.date" content="2026-04-23">
+    <meta name="dcterms.date" content="2026-04-27">
     <meta name="keywords" content="Machine Learning, Materials Science, Materials Processing, Materials Characterization, Deep Learning, Microstructure Analysis, Process Optimization">
 
     <title>Machine Learning in Materials Processing &amp; Characterization</title>
@@ -166,10 +166,10 @@
 ">
 <meta name="citation_keywords" content="Machine Learning,Materials Science,Materials Processing,Materials Characterization,Deep Learning,Microstructure Analysis,Process Optimization">
 <meta name="citation_author" content="Philipp Pelz">
-<meta name="citation_publication_date" content="2026-04-23">
-<meta name="citation_cover_date" content="2026-04-23">
+<meta name="citation_publication_date" content="2026-04-27">
+<meta name="citation_cover_date" content="2026-04-27">
 <meta name="citation_year" content="2026">
-<meta name="citation_online_date" content="2026-04-23">
+<meta name="citation_online_date" content="2026-04-27">
 <meta name="citation_language" content="en">
 <meta name="citation_reference" content="citation_title=Strategies for the development of volcanic hazard maps in monogenetic volcanic fields: The example of La Palma (Canary Islands);,citation_author=José Marrero;,citation_author=Alicia García;,citation_author=Manuel Berrocoso;,citation_author=Ángeles Llinares;,citation_author=Antonio Rodríguez-Losada;,citation_author=R. Ortiz;,citation_publication_date=2019-07;,citation_cover_date=2019-07;,citation_year=2019;,citation_doi=10.1186/s13617-019-0085-5;,citation_volume=8;,citation_journal_title=Journal of Applied Volcanology;">
 <meta name="citation_reference" content="citation_title=Materials data science: Introduction to data mining, machine learning, and data-driven predictions for materials science and engineering;,citation_author=Stefan Sandfeld;,citation_publication_date=2024;,citation_cover_date=2024;,citation_year=2024;">
@@ -214,7 +214,7 @@ <h1 class="title">Machine Learning in Materials Processing &amp; Characterizatio
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">April 23, 2026</p>
+              <p class="date">April 27, 2026</p>
             </div>
           </div>
 
@@ -335,7 +335,7 @@ <h1>Machine Learning in Materials Processing &amp; Characterization</h1>
     <div class="course-meta-item" style="grid-column: 1 / -1;"><div class="course-meta-label">Prerequisites</div><div class="course-meta-value">Helpful: Mathematical Foundations of AI &amp; ML or equivalent background</div></div>
   </div>
   <div class="course-actions">
-    <a href="https://www.studon.fau.de/" class="btn btn-primary">StudOn</a>
+    <a href="https://www.studon.fau.de/campo/course/538616" class="btn btn-primary">StudOn</a>
     <a href="https://github.com/ECLIPSE-Lab/MachineLearningForCharacterizationAndProcessing" class="btn btn-outline-secondary">GitHub / Materials</a>
     <a href="https://pelzlab.science/teaching.html" class="btn btn-outline-secondary">All Teaching</a>
     <a href="https://www.mat.studium.fau.de/studiengaenge/neu-ki-materialtechnologie/" class="btn btn-outline-secondary">KI in Materialtechnologie</a>

index-preview.html

@@ -9,11 +9,11 @@
     "Course Curriculum and Materials\n",
     "\n",
     "Philipp Pelz (Materials Science and Engineering)  \n",
-    "April 23, 2026\n",
+    "April 27, 2026\n",
     "\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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     "    <div class=\"course-meta-item\" style=\"grid-column: 1 / -1;\"><div class=\"course-meta-label\">Prerequisites</div><div class=\"course-meta-value\">Helpful: Mathematical Foundations of AI & ML or equivalent background</div></div>\n",
     "  </div>\n",
     "  <div class=\"course-actions\">\n",
-    "    <a href=\"https://www.studon.fau.de/\" class=\"btn btn-primary\">StudOn</a>\n",
+    "    <a href=\"https://www.studon.fau.de/campo/course/538616\" class=\"btn btn-primary\">StudOn</a>\n",
     "    <a href=\"https://github.com/ECLIPSE-Lab/MachineLearningForCharacterizationAndProcessing\" class=\"btn btn-outline-secondary\">GitHub / Materials</a>\n",
     "    <a href=\"https://pelzlab.science/teaching.html\" class=\"btn btn-outline-secondary\">All Teaching</a>\n",
     "    <a href=\"https://www.mat.studium.fau.de/studiengaenge/neu-ki-materialtechnologie/\" class=\"btn btn-outline-secondary\">KI in Materialtechnologie</a>\n",
@@ -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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@@ -383,7 +383,7 @@
     "\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

@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Philipp Pelz">
-<meta name="dcterms.date" content="2026-04-23">
+<meta name="dcterms.date" content="2026-04-27">
 <meta name="keywords" content="Machine Learning, Materials Science, Materials Processing, Materials Characterization, Deep Learning, Microstructure Analysis, Process Optimization">
 
 <title>Machine Learning in Materials Processing &amp; Characterization</title>
@@ -79,10 +79,10 @@
 ">
 <meta name="citation_keywords" content="Machine Learning,Materials Science,Materials Processing,Materials Characterization,Deep Learning,Microstructure Analysis,Process Optimization">
 <meta name="citation_author" content="Philipp Pelz">
-<meta name="citation_publication_date" content="2026-04-23">
-<meta name="citation_cover_date" content="2026-04-23">
+<meta name="citation_publication_date" content="2026-04-27">
+<meta name="citation_cover_date" content="2026-04-27">
 <meta name="citation_year" content="2026">
-<meta name="citation_online_date" content="2026-04-23">
+<meta name="citation_online_date" content="2026-04-27">
 <meta name="citation_language" content="en">
 <meta name="citation_reference" content="citation_title=Strategies for the development of volcanic hazard maps in monogenetic volcanic fields: The example of La Palma (Canary Islands);,citation_author=José Marrero;,citation_author=Alicia García;,citation_author=Manuel Berrocoso;,citation_author=Ángeles Llinares;,citation_author=Antonio Rodríguez-Losada;,citation_author=R. Ortiz;,citation_publication_date=2019-07;,citation_cover_date=2019-07;,citation_year=2019;,citation_doi=10.1186/s13617-019-0085-5;,citation_volume=8;,citation_journal_title=Journal of Applied Volcanology;">
 <meta name="citation_reference" content="citation_title=Materials data science: Introduction to data mining, machine learning, and data-driven predictions for materials science and engineering;,citation_author=Stefan Sandfeld;,citation_publication_date=2024;,citation_cover_date=2024;,citation_year=2024;">
@@ -120,7 +120,7 @@ <h1 class="title">Machine Learning in Materials Processing &amp; Characterizatio
                 <div>
             <div class="quarto-title-meta-heading">Published</div>
             <div class="quarto-title-meta-contents">
-              <p class="date">April 23, 2026</p>
+              <p class="date">April 27, 2026</p>
             </div>
           </div>
 
@@ -246,7 +246,7 @@ <h1>Machine Learning in Materials Processing &amp; Characterization</h1>
     <div class="course-meta-item" style="grid-column: 1 / -1;"><div class="course-meta-label">Prerequisites</div><div class="course-meta-value">Helpful: Mathematical Foundations of AI &amp; ML or equivalent background</div></div>
   </div>
   <div class="course-actions">
-    <a href="https://www.studon.fau.de/" class="btn btn-primary">StudOn</a>
+    <a href="https://www.studon.fau.de/campo/course/538616" class="btn btn-primary">StudOn</a>
     <a href="https://github.com/ECLIPSE-Lab/MachineLearningForCharacterizationAndProcessing" class="btn btn-outline-secondary">GitHub / Materials</a>
     <a href="https://pelzlab.science/teaching.html" class="btn btn-outline-secondary">All Teaching</a>
     <a href="https://www.mat.studium.fau.de/studiengaenge/neu-ki-materialtechnologie/" class="btn btn-outline-secondary">KI in Materialtechnologie</a>

index.embed.ipynb

@@ -9,11 +9,11 @@
     "Course Curriculum and Materials\n",
     "\n",
     "Philipp Pelz (Materials Science and Engineering)  \n",
-    "April 23, 2026\n",
+    "April 27, 2026\n",
     "\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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     "    <div class=\"course-meta-item\" style=\"grid-column: 1 / -1;\"><div class=\"course-meta-label\">Prerequisites</div><div class=\"course-meta-value\">Helpful: Mathematical Foundations of AI & ML or equivalent background</div></div>\n",
     "  </div>\n",
     "  <div class=\"course-actions\">\n",
-    "    <a href=\"https://www.studon.fau.de/\" class=\"btn btn-primary\">StudOn</a>\n",
+    "    <a href=\"https://www.studon.fau.de/campo/course/538616\" class=\"btn btn-primary\">StudOn</a>\n",
     "    <a href=\"https://github.com/ECLIPSE-Lab/MachineLearningForCharacterizationAndProcessing\" class=\"btn btn-outline-secondary\">GitHub / Materials</a>\n",
     "    <a href=\"https://pelzlab.science/teaching.html\" class=\"btn btn-outline-secondary\">All Teaching</a>\n",
     "    <a href=\"https://www.mat.studium.fau.de/studiengaenge/neu-ki-materialtechnologie/\" class=\"btn btn-outline-secondary\">KI in Materialtechnologie</a>\n",
@@ -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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     "\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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index.html

@@ -120,7 +120,7 @@ execute:
     <div class="course-meta-item" style="grid-column: 1 / -1;"><div class="course-meta-label">Prerequisites</div><div class="course-meta-value">Helpful: Mathematical Foundations of AI & ML or equivalent background</div></div>
   </div>
   <div class="course-actions">
-    <a href="https://www.studon.fau.de/" class="btn btn-primary">StudOn</a>
+    <a href="https://www.studon.fau.de/campo/course/538616" class="btn btn-primary">StudOn</a>
     <a href="https://github.com/ECLIPSE-Lab/MachineLearningForCharacterizationAndProcessing" class="btn btn-outline-secondary">GitHub / Materials</a>
     <a href="https://pelzlab.science/teaching.html" class="btn btn-outline-secondary">All Teaching</a>
     <a href="https://www.mat.studium.fau.de/studiengaenge/neu-ki-materialtechnologie/" class="btn btn-outline-secondary">KI in Materialtechnologie</a>

index.out.ipynb

@@ -68,7 +68,7 @@
     "**Summary for ML-PC Week 10:**  \n",
     "This unit focuses on the processing of high-dimensional **Characterization Signals** (like XRD, EDS, and EELS) using unsupervised learning. We introduce **K-Means Clustering** and **t-SNE** for the automatic identification and visualization of phases in large experimental libraries. We then explore **Autoencoders**—neural networks that learn to compress complex spectra into a low-dimensional “latent space.” This allows for advanced denoising and feature extraction, enabling scientists to handle the massive data volumes produced by modern high-throughput characterization tools without losing physical insight."
    ],
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