This repository is a collection of notebooks about Bayesian Machine Learning. The following links display
some of the notebooks via nbviewer to ensure a proper rendering of formulas.
Dependencies are specified in requirements.txt
files in subdirectories.
Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation.
Gaussian processes. Introduction to Gaussian processes for regression. Implementation with plain NumPy/SciPy as well as with scikit-learn and GPy.
Gaussian processes for classification. Introduction to Gaussian processes for classification. Implementation with plain NumPy/SciPy as well as with scikit-learn.
Sparse Gaussian processes. Introduction to sparse Gaussian processes using a variational approach. Example implementation with JAX.
Bayesian optimization. Introduction to Bayesian optimization. Implementation with plain NumPy/SciPy as well as with libraries scikit-optimize and GPyOpt. Hyper-parameter tuning as application example.
Variational inference in Bayesian neural networks. Demonstrates how to implement a Bayesian neural network and variational inference of weights. Example implementation with Keras.
Reliable uncertainty estimates for neural network predictions. Uses noise contrastive priors for Bayesian neural networks to get more reliable uncertainty estimates for OOD data. Implemented with Tensorflow 2 and Tensorflow Probability.
Latent variable models, part 1: Gaussian mixture models and the EM algorithm. Introduction to the expectation maximization (EM) algorithm and its application to Gaussian mixture models. Implementation with plain NumPy/SciPy and scikit-learn. See also PyMC3 implementation.
Latent variable models, part 2: Stochastic variational inference and variational autoencoders. Introduction to stochastic variational inference with a variational autoencoder as application example. Implementation with Tensorflow 2.x.
Deep feature consistent variational autoencoder. Describes how a perceptual loss can improve the quality of images generated by a variational autoencoder. Example implementation with Keras.
Conditional generation via Bayesian optimization in latent space. Describes an approach for conditionally generating outputs with desired properties by doing Bayesian optimization in latent space learned by a variational autoencoder. Example application implemented with Keras and GPyOpt.