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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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<li><ahref="#unit-i-experimental-data-as-a-learning-problem-weeks-13" id="toc-unit-i-experimental-data-as-a-learning-problem-weeks-13" class="nav-link" data-scroll-target="#unit-i-experimental-data-as-a-learning-problem-weeks-13"><spanclass="header-section-number">1.3.1</span> Unit I — Experimental Data as a Learning Problem (Weeks 1–3)</a></li>
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<li><ahref="#unit-ii-representation-learning-for-microstructures-weeks-46" id="toc-unit-ii-representation-learning-for-microstructures-weeks-46" class="nav-link" data-scroll-target="#unit-ii-representation-learning-for-microstructures-weeks-46"><spanclass="header-section-number">1.3.2</span> Unit II — Representation Learning for Microstructures (Weeks 4–6)</a></li>
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<li><ahref="#unit-iii-learning-from-processing-data-weeks-89" id="toc-unit-iii-learning-from-processing-data-weeks-89" class="nav-link" data-scroll-target="#unit-iii-learning-from-processing-data-weeks-89"><spanclass="header-section-number">1.3.3</span> Unit III — Learning from Processing Data (Weeks 8–9)</a></li>
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<li><ahref="#unit-iv-characterization-transformers-and-uncertainty-weeks-1012" id="toc-unit-iv-characterization-transformers-and-uncertainty-weeks-1012" class="nav-link" data-scroll-target="#unit-iv-characterization-transformers-and-uncertainty-weeks-1012"><spanclass="header-section-number">1.3.4</span> Unit IV — Characterization, Transformers, and Uncertainty (Weeks 10–12)</a></li>
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<li><ahref="#unit-v-physics-trust-and-synthesis-weeks-1314" id="toc-unit-v-physics-trust-and-synthesis-weeks-1314" class="nav-link" data-scroll-target="#unit-v-physics-trust-and-synthesis-weeks-1314"><spanclass="header-section-number">1.3.5</span> Unit V — Physics, Trust, and Synthesis (Weeks 13–14)</a></li>
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<li><ahref="#unit-iv-characterization-transformers-and-physics-weeks-1012" id="toc-unit-iv-characterization-transformers-and-physics-weeks-1012" class="nav-link" data-scroll-target="#unit-iv-characterization-transformers-and-physics-weeks-1012"><spanclass="header-section-number">1.3.4</span> Unit IV — Characterization, Transformers, and Physics (Weeks 10–12)</a></li>
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<li><ahref="#unit-v-uncertainty-trust-and-synthesis-weeks-1314" id="toc-unit-v-uncertainty-trust-and-synthesis-weeks-1314" class="nav-link" data-scroll-target="#unit-v-uncertainty-trust-and-synthesis-weeks-1314"><spanclass="header-section-number">1.3.5</span> Unit V — Uncertainty, Trust, and Synthesis (Weeks 13–14)</a></li>
<h3data-number="1.3.4" class="anchored" data-anchor-id="unit-iv-characterization-transformers-and-uncertainty-weeks-1012"><spanclass="header-section-number">1.3.4</span> Unit IV — Characterization, Transformers, and Uncertainty (Weeks 10–12)</h3>
<h3data-number="1.3.4" class="anchored" data-anchor-id="unit-iv-characterization-transformers-and-physics-weeks-1012"><spanclass="header-section-number">1.3.4</span> Unit IV — Characterization, Transformers, and Physics (Weeks 10–12)</h3>
<h4data-number="1.3.4.1" class="anchored" data-anchor-id="week-10-ml-for-characterization-signals"><spanclass="header-section-number">1.3.4.1</span> Week 10 – ML for characterization signals</h4>
<p><strong>Exercise:</strong> Apply a small ViT / attention model to a characterization dataset (e.g. 4D-STEM patches); compare against a CNN baseline.</p>
<h3data-number="1.3.5" class="anchored" data-anchor-id="unit-v-physics-trust-and-synthesis-weeks-1314"><spanclass="header-section-number">1.3.5</span> Unit V — Physics, Trust, and Synthesis (Weeks 13–14)</h3>
<h3data-number="1.3.5" class="anchored" data-anchor-id="unit-v-uncertainty-trust-and-synthesis-weeks-1314"><spanclass="header-section-number">1.3.5</span> Unit V — Uncertainty, Trust, and Synthesis (Weeks 13–14)</h3>
<p>The Tuesday lecture does not take place. This unit — <strong>physics-informed and constrained ML</strong> — is <strong>required self-study</strong> from the slide deck below. It remains examinable and is the unit that delivers the “integrate physical constraints into ML workflows” learning outcome. The Thursday exercise runs as normal.</p>
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<p>The Tuesday lecture does not take place. This unit — <strong>uncertainty-aware regression & Gaussian Processes</strong> — is <strong>required self-study</strong> from the slide deck below. It remains examinable and is the unit that delivers the “quantify and propagate predictive uncertainty” learning outcome. The Thursday exercise runs as normal.</p>
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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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"June 21, 2026\n",
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"July 5, 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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"id": "908daa1c-6d96-48dc-bb7e-c8663997068a"
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"id": "5410d27f-40c0-4eb4-a61b-18817791a842"
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"}\n",
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"</style>"
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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",
"*Lecture cancelled (Tuesday, 07.07.2026) — delivered as required self-study. Exercise: Thursday, 09.07.2026, 16:15-17:45 takes place as scheduled.*\n",
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"> **No lecture on 07.07.2026**\n",
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"> The Tuesday lecture does not take place. This unit — **physics-informed and constrained ML** — is **required self-study** from the slide deck below. It remains examinable and is the unit that delivers the “integrate physical constraints into ML workflows” learning outcome. The Thursday exercise runs as normal.\n",
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"> The Tuesday lecture does not take place. This unit — **uncertainty-aware regression & Gaussian Processes** — is **required self-study** from the slide deck below. It remains examinable and is the unit that delivers the “quantify and propagate predictive uncertainty” learning outcome. The Thursday exercise runs as normal.\n",
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