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‎.nojekyll‎

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‎index-meca.zip‎

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‎index-preview.html‎

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<title>Machine Learning in Materials Processing &amp; Characterization</title>
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<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;">
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<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;">
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<div class="quarto-title-meta-heading">Published</div>
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<div class="quarto-title-meta-contents">
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<p class="date">June 16, 2026</p>
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<section id="week-6-data-scarcity-transfer-learning" class="level4" data-number="1.3.2.3">
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<h4 data-number="1.3.2.3" class="anchored" data-anchor-id="week-6-data-scarcity-transfer-learning"><span class="header-section-number">1.3.2.3</span> Week 6 – Data scarcity &amp; transfer learning</h4>
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<p><em>Lecture: Tuesday, 19.05.2026, 14:15-15:45 | Exercise: Thursday, 21.05.2026, 16:15-17:45</em></p>
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<p><strong>Slides:</strong> <a href="https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/01_intro.html">Open</a></p>
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<p><strong>Slides:</strong> <a href="https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/06_transfer_learning.html">Open</a></p>
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<li>Why materials datasets are small.</li>
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<li>Transfer learning from natural images vs self-supervised pretraining.</li>

‎index.docx‎

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‎index.embed.ipynb‎

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"Philipp Pelz (Materials Science and Engineering) \n",
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"May 24, 2026\n",
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"June 16, 2026\n",
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"\n",
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"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",
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"\n",
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"**Slides:** [Open](https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/01_intro.html)\n",
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"\n",
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"- Why materials datasets are small.\n",
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"- Transfer learning from natural images vs self-supervised pretraining.\n",
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"- ML-assisted autofocus or EBSD pattern classification.\n",
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"- Multi-modal fusion of images, spectra, and process parameters."
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‎index.html‎

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<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;">
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<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;">
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<section id="week-6-data-scarcity-transfer-learning" class="level4" data-number="1.3.2.3">
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<h4 data-number="1.3.2.3" class="anchored" data-anchor-id="week-6-data-scarcity-transfer-learning"><span class="header-section-number">1.3.2.3</span> Week 6 – Data scarcity &amp; transfer learning</h4>
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<p><em>Lecture: Tuesday, 19.05.2026, 14:15-15:45 | Exercise: Thursday, 21.05.2026, 16:15-17:45</em></p>
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<p><strong>Slides:</strong> <a href="https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/01_intro.html">Open</a></p>
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<p><strong>Slides:</strong> <a href="https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/06_transfer_learning.html">Open</a></p>
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‎index.out.ipynb‎

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"Course Curriculum and Materials\n",
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"June 16, 2026\n",
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"\n",
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"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",
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"\n",
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"*Lecture: Tuesday, 19.05.2026, 14:15-15:45 \\| Exercise: Thursday, 21.05.2026, 16:15-17:45*\n",
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"\n",
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"**Slides:** [Open](https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/01_intro.html)\n",
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"**Slides:** [Open](https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/06_transfer_learning.html)\n",
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"- Why materials datasets are small.\n",
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"- Transfer learning from natural images vs self-supervised pretraining.\n",
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"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.qmd‎

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*Lecture: Tuesday, 19.05.2026, 14:15-15:45 | Exercise: Thursday, 21.05.2026, 16:15-17:45*
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**Slides:** [Open](https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/01_intro.html)
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**Slides:** [Open](https://pelzlab.science/public_presentations/ml_for_characterization_and_processing/unit06_transfer_learning/06_transfer_learning.html)
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- Why materials datasets are small.
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