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<metaname="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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<metaname="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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<h4data-number="1.3.1.1" class="anchored" data-anchor-id="week-1-what-makes-materials-data-special"><spanclass="header-section-number">1.3.1.1</span> Week 1 – What makes materials data special?</h4>
<h4data-number="1.3.1.2" class="anchored" data-anchor-id="week-2-physics-of-data-formation"><spanclass="header-section-number">1.3.1.2</span> Week 2 – Physics of data formation</h4>
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"Course Curriculum and Materials\n",
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"\n",
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"Philipp Pelz (Materials Science and Engineering) \n",
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"March 23, 2026\n",
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"April 23, 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",
"- Image and signal formation in characterization: resolution, contrast, artifacts.\n",
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"- Sampling, aliasing, noise as *physical priors* (not preprocessing tricks).\n",
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"- Relation to MFML refresher on PCA and covariance.\n",
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"**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."
<metaname="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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<metaname="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 & Characterizatio
<h4data-number="1.3.1.1" class="anchored" data-anchor-id="week-1-what-makes-materials-data-special"><spanclass="header-section-number">1.3.1.1</span> Week 1 – What makes materials data special?</h4>
<h4data-number="1.3.1.2" class="anchored" data-anchor-id="week-2-physics-of-data-formation"><spanclass="header-section-number">1.3.1.2</span> Week 2 – Physics of data formation</h4>
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"Course Curriculum and Materials\n",
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"\n",
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"Philipp Pelz (Materials Science and Engineering) \n",
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"March 23, 2026\n",
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"April 23, 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",
"- Image and signal formation in characterization: resolution, contrast, artifacts.\n",
161
165
"- Sampling, aliasing, noise as *physical priors* (not preprocessing tricks).\n",
162
166
"- Relation to MFML refresher on PCA and covariance.\n",
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"\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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