| Literatur |
- Gbur, Gregory J. Mathematical methods for optical physics and engineering. Cambridge University Press, 2011.
- Brunton, Steven L., and J. Nathan Kutz. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2019.
- Strang, Gilbert. Linear algebra and learning from data. Cambridge: Wellesley-Cambridge Press, 2019.
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| Lerninhalte |
- Review: Linear Algebra, Calculus, Python
- Optimization part 1: Continuous (Euler Lagrange) and Discrete (multivariate calculus)
- Programming lab: genetic algorithms + Fermat principle
- Optimization part 2: nonlinear optimization, regularization, Lagrange multipliers
- Optimization part 3: Convex techniques, l1 minimization
- Programming lab: single pixel camera
- Optimization part 4: Automatic differentiation
- Matrix representation of coherent optical systems
- Programming lab: keras toolbox, optical eigenmodes
- Multiple scattering: Born / Rytov series, beam propagation method
- Tomographic inversion
- Programming lab: Foldy-Lax scattering theory
- Phase retrieval part 1: coherent diffraction imaging (CDI)
- Phase retrieval part 2: ptychography
- Programming lab: hybrid input output, shrink wrap, ptychography
- Phase retrieval part 3: Fourier ptychography
- Image deconvolution: structured illumination microscopy, pupil engineering
- Programming lab: extended depth-of-field systems
- Imaging with spatially partially coherent light
- Parameter estimation: Fisher information and Cramer Rao lower bound
- Programming lab: Coded aperture imaging, resolution assessment, edge responses, modulation transfer function, Fourier ring correlation
- Neural networks part 1: Image classification
- Neural networks part 2: Image regression
- Programming lab: digit recognition, counting red blood cells
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