Atomic-scale Modeling: Interactive Exercises and Graded Assignments

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Principal Component Analysis: Applications and Limitations

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.

Data Representation: PCA

Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Unsupervised Learning: Dimensionality Reduction

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Solid State Characterization

Covers state-of-the-art methods for solid state characterization and emphasizes choosing the appropriate method for different materials.

Electron Diffraction: Basics and Applications

Covers the fundamentals of electron diffraction and its applications in understanding crystal structures and symmetry, including lattice vectors, lattice planes, and dark-field imaging techniques.

Multiple beam scattering in electron diffraction

Explores multiple beam scattering in electron diffraction, focusing on zone axis diffraction patterns and theoretical approaches.

TEM imaging with diffraction contrast

Covers the basics of electron diffraction, diffraction theory, image formation, and X-ray diffraction techniques.

Diffraction Basics IV: Bragg's Law and Rotations

Explains Bragg's law, rotations in space, and the Ewald sphere for diffraction.

Fourier transforms: Light-matter interaction

Explores Fourier and Laplace transforms in materials science, focusing on light-matter interaction, diffraction patterns, and crystal properties.