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 \usepackage{longtable,booktabs,array}
+\newcounter{none} % for unnumbered tables
 \usepackage{calc} % for calculating minipage widths
 % Correct order of tables after \paragraph or \subparagraph
 \usepackage{etoolbox}
@@ -291,6 +292,14 @@ \subsubsection{Unit I --- Experimental Data as a Learning Problem (Weeks
   Why ML failure modes are common in experimental science.
 \end{itemize}
 
+\textbf{Summary:} This unit introduces the transition from classical
+physics-based modeling to data-driven discovery in materials science. We
+explore the unique challenges of experimental materials data, including
+its multi-modal nature, high acquisition cost, and the fundamental
+Processing-Structure-Property-Performance (PSPP) relationships. Key
+concepts include data scales, measurement uncertainty, and the CRISP-DM
+process adapted for scientific workflows.
+
 \textbf{Exercise:}\\
 Inspect real microscopy and process datasets; identify sources of bias
 and noise.
@@ -315,6 +324,15 @@ \subsubsection{Unit I --- Experimental Data as a Learning Problem (Weeks
   Relation to MFML refresher on PCA and covariance.
 \end{itemize}
 
+\textbf{Summary:} This unit bridges the gap between the physical process
+of data acquisition and the mathematical tools used to describe it. We
+analyze how signals are formed in characterization tools and how
+physical constraints (resolution, noise, sampling) act as priors for
+learning. We then introduce Principal Component Analysis (PCA) and
+Singular Value Decomposition (SVD) as fundamental techniques for
+discovering low-dimensional structure in high-dimensional experimental
+datasets.
+
 \textbf{Exercise:}\\
 Fourier inspection of micrographs; effects of sampling and filtering.
 
@@ -336,6 +354,15 @@ \subsubsection{Unit I --- Experimental Data as a Learning Problem (Weeks
   Why ``good accuracy'' often means a broken pipeline.
 \end{itemize}
 
+\textbf{Summary:} This unit focuses on the most critical and often
+overlooked part of the ML pipeline: data integrity. We discuss
+systematic data cleaning and normalization techniques while highlighting
+the unique challenges of labeling experimental materials data, such as
+inter-annotator variance. A major focus is on \textbf{Data Leakage},
+specifically how spatial and physical correlations in materials samples
+can lead to deceptively high model performance. We introduce robust
+validation strategies to ensure models generalize to truly unseen data.
+
 \textbf{Exercise:}\\
 Construct a deliberately flawed ML pipeline and diagnose its failure.
 
@@ -363,6 +390,16 @@ \subsubsection{Unit II --- Representation Learning for Microstructures
   Transition to learned representations.
 \end{itemize}
 
+\textbf{Summary:} This unit marks the transition from classical,
+hand-crafted microstructure quantification (like grain size and phase
+fractions) to the modern paradigm of \textbf{learned representations}.
+We first review traditional stereological metrics and their limitations
+in capturing complex structural nuances. We then introduce the
+foundational unit of modern ML: the \textbf{artificial neuron}. By
+understanding weights, biases, and non-linear activation functions, we
+build the framework for Multi-Layer Perceptrons (MLPs) that can
+automatically learn optimal features from materials data.
+
 \textbf{Exercise:}\\
 Compare classical features vs simple NN-based features for
 microstructure tasks.
@@ -386,6 +423,16 @@ \subsubsection{Unit II --- Representation Learning for Microstructures
   Overfitting risks with small datasets.
 \end{itemize}
 
+\textbf{Summary:} This unit introduces \textbf{Convolutional Neural
+Networks (CNNs)}, the workhorse of modern computer vision, and applies
+them to materials characterization. We explore how convolutions allow
+networks to automatically learn hierarchical structure detectors---from
+simple edges to complex phase morphologies---while drastically reducing
+the number of parameters compared to standard MLPs. Through case studies
+in phase segmentation and defect detection, students learn the intuition
+behind filters, pooling, and the unique challenges of applying deep
+learning to high-resolution, noisy experimental micrographs.
+
 \textbf{Exercise:}\\
 Train a small CNN on microstructure images; analyze failure cases.
 
@@ -407,6 +454,16 @@ \subsubsection{Unit II --- Representation Learning for Microstructures
   When transfer learning helps---and when it does not.
 \end{itemize}
 
+\textbf{Summary:} This unit addresses the fundamental bottleneck of
+materials informatics: \textbf{Data Scarcity}. We explore how to build
+powerful deep learning models when only a few hundred labeled images or
+signals are available. The core focus is on \textbf{Transfer Learning},
+where we leverage knowledge from models pretrained on millions of
+natural images to accelerate learning and improve generalization on
+materials tasks. We also cover \textbf{Data Augmentation} strategies
+tailored for scientific data and discuss when and why transferring
+knowledge across different physical domains succeeds or fails.
+
 \textbf{Exercise:}\\
 Fine-tune a pretrained model; compare against training from scratch.
 
@@ -432,6 +489,17 @@ \subsubsection{Unit III --- Learning from Processing Data (Weeks
   Relation to MFML concepts of generalization.
 \end{itemize}
 
+\textbf{Summary:} This unit explores the application of machine learning
+to \textbf{Time-Series Data}, specifically for monitoring and predicting
+materials processing outcomes. We introduce \textbf{Recurrent Neural
+Networks (RNNs)} and their advanced variants like \textbf{LSTMs}, which
+are designed to handle sequential dependencies. We discuss the critical
+preprocessing steps of signal smoothing and triggering required to
+handle noisy experimental logs. Through case studies in additive
+manufacturing and process stability, students learn how to build models
+that ``remember'' the processing history to predict future states and
+detect anomalies in real-time.
+
 \textbf{Exercise:}\\
 Predict a process outcome from time-series data using regression or
 simple RNNs.
@@ -454,6 +522,16 @@ \subsubsection{Unit III --- Learning from Processing Data (Weeks
   Robustness as a design criterion.
 \end{itemize}
 
+\textbf{Summary:} This unit shifts the focus from model performance to
+\textbf{Model Reliability}. We explore the Bias-Variance tradeoff and
+the fundamental challenge of generalization---ensuring that an ML model
+works on new, unseen data from the factory floor. We introduce robust
+validation techniques like K-Fold and Stratified Cross-Validation to
+stabilize performance estimates on small materials datasets. A key focus
+is on \textbf{Process Robustness}, where we use sensitivity analysis to
+identify ``Process Windows''---regions in parameter space where material
+quality is maximized and insensitive to industrial noise.
+
 \textbf{Exercise:}\\
 Analyze model robustness under perturbed process conditions.
 
@@ -475,6 +553,17 @@ \subsubsection{Unit III --- Learning from Processing Data (Weeks
   Physics-informed vs unconstrained regression.
 \end{itemize}
 
+\textbf{Summary:} This unit explores \textbf{Inverse Problems}---the
+cornerstone of materials design where we seek the processing parameters
+required to achieve a target microstructure or performance. We contrast
+these with causal forward problems and discuss why they are often
+ill-posed and multi-valued. We introduce \textbf{Physics-Informed
+Learning} as a way to solve these challenges by enriching models with
+physical transformations and constraints. Students learn how to build
+and interpret \textbf{Process Maps} and ``Process Corridors,'' using
+machine learning to visualize safe operating regions in complex
+experimental spaces.
+
 \textbf{Exercise:}\\
 Construct a simple ML-based process map; compare constrained vs
 unconstrained models.
@@ -501,6 +590,17 @@ \subsubsection{Unit IV --- Uncertainty, Surrogates, and Automation
   Using ML without destroying physical meaning.
 \end{itemize}
 
+\textbf{Summary:} This unit focuses on the processing of
+high-dimensional \textbf{Characterization Signals} (like XRD, EDS, and
+EELS) using unsupervised learning. We introduce \textbf{K-Means
+Clustering} and \textbf{t-SNE} for the automatic identification and
+visualization of phases in large experimental libraries. We then explore
+\textbf{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.
+
 \textbf{Exercise:}\\
 Apply PCA/NMF to spectral datasets; interpret components physically.
 
@@ -647,7 +747,19 @@ \subsection{Lab Possibilities}\label{lab-possibilities}
   Multi-modal fusion of images, spectra, and process parameters.
 \end{itemize}
 
-\phantomsection\label{refs}
+\textbf{Summary:} This unit explores the cutting edge of
+\textbf{Autonomous Characterization}, where machine learning moves from
+passive data analysis to active instrument control. We introduce
+\textbf{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 \textbf{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.
+
+\protect\phantomsection\label{refs}
 \begin{CSLReferences}{1}{0}
 \bibitem[\citeproctext]{ref-sandfeld2024materials}
 Sandfeld, S. (2024). \emph{Materials data science: Introduction to data