https://github.com/aidancrilly/ml_lecture_demos
Recordings, slides and code demos used in postgraduate lecture on Machine Learning, focusing on regression and inference tasks.
Science Score: 26.0%
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Low similarity (6.7%) to scientific vocabulary
Repository
Recordings, slides and code demos used in postgraduate lecture on Machine Learning, focusing on regression and inference tasks.
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Metadata Files
README.md
Code demos from "Machine Learning Basics" Lecture from PG series
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)
The required python library requirements are given in requirements.txt which can be pip installed:
pip install -r requirements.txt
Lecture recordings on YouTube:
2024:
2023:
Owner
- Name: Aidan Crilly
- Login: aidancrilly
- Kind: user
- Repositories: 3
- Profile: https://github.com/aidancrilly
Schmidt Future AI in Science Postdoctoral Fellow, I-X, Imperial College London
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