Author : Aidan Crilly
Repository storing Python code examples from lecture and copy of slides.
Demonstrations include:
- Ordinary least squares in spectral analysis
- Deconvolution and Tikonhov regularisation
- Non-linear least squares and optimisation
- Laplace's method of uncertainty quantification (using differentiable programming)
- Markov Chain Monte Carlo with Metropolis algorithm
- Gaussian processes
- Bayesian Optimisation
- Neural networks (Multi-layer perceptron and Physics Informed NN)
- K-means clustering
The required python library requirements are given in requirements.txt which can be pip installed:
pip install -r requirements.txt
Lecture recordings on YouTube:
2025:
2024:
2023: