Bayesian Methods for Machine Learning As part of this Coursera spetialization we implemented different algorithms like:
- Expectation maximization for Gaussian Mixture Models (GMMs)
- Applied Variational Inference in a Variational AutoEncoder (VAE) architecture using Convolutional Networks
- Implemented a basic Monte Carlo Simulation to estimate probabilities and used MCMC to perform inference using PyMC3
- Performed regression tasks and hyperparameters optimization using Gaussian Processes.
All the assignments were done in Ipython notebooks.