Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.
Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.
Introduces a programming course for engineers, emphasizing the importance of mastering multiple languages for future projects.
Covers eigenvalues, eigenfunctions, Hermitian operators, and the measurement of observables in quantum chemistry.
