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‎.nojekyll‎

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‎_tex/index.tex‎

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Train/test leakage in materials workflows.
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\end{itemize}
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\emph{(These first three weeks ensure students understand why ML behaves
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differently on materials data compared to CIFs and DFT databases in the
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other course.)}
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\subsubsection{Unit II --- ML for Microstructure: Vision \&
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Representation (Weeks
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4--6)}\label{unit-ii-ml-for-microstructure-vision-representation-weeks-46}
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Physics-informed ML vs naive regression.
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\end{itemize}
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\emph{(This unit ensures profoundly processing-centered ML
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content---distinct from genomics's structure-first world.)}
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\subsubsection{Unit IV --- ML for Characterization Signals (Weeks
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10--12)}\label{unit-iv-ml-for-characterization-signals-weeks-1012}
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Early vs late fusion.
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\end{itemize}
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\emph{(This module draws synergy with your research---students love
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learning real lab-relevant problems.)}
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\subsubsection{Unit V --- Project + Reflection (Weeks
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13--14)}\label{unit-v-project-reflection-weeks-1314}
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‎index-meca.zip‎

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‎index.docx‎

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‎index.html‎

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<li>Annotation, segmentation, inter-annotator variance.</li>
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<li>Train/test leakage in materials workflows.</li>
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<p><em>(These first three weeks ensure students understand why ML behaves differently on materials data compared to CIFs and DFT databases in the other course.)</em></p>
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</section>
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<li>ML-guided process maps (AM: laser power vs scan speed; metallurgy: TTT/CCT approximations).</li>
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<li>Physics-informed ML vs naive regression.</li>
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</ul>
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<p><em>(This unit ensures profoundly processing-centered ML content—distinct from genomics’s structure-first world.)</em></p>
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<li>Combining images + spectra + process parameters.</li>
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<li>Early vs late fusion.</li>
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<p><em>(This module draws synergy with your research—students love learning real lab-relevant problems.)</em></p>
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‎index.pdf‎

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