Final Project from the course "Probabilistic Machine Learning" @ Data Science & Scientific Computing, University of Trieste, year 2020/2021, written in ipynb.
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Updated
Oct 23, 2021 - Jupyter Notebook
Final Project from the course "Probabilistic Machine Learning" @ Data Science & Scientific Computing, University of Trieste, year 2020/2021, written in ipynb.
Exploration of major kinds of statistical learning models and algorithms used in data analysis. Clustering, Neural Networks, Probabilistic ML are a few of the topics.
Machine Learning Code as per my understanding
Code for the research paper Meta-learning with hierarchical models based on similarity of causal mechanisms
We present a probabilistic model for neural spike counts that can capture arbitrary single neuron and joint statistics with their modulation by external covariates.
Predicting air pollution amounts in cities using a Gaussian Process model
Repository for the Probabilistic Machine Learning labs/practica (@ UniTS, Spring 2023)
Predicting Mortality after Transcatheter Aortic Valve Replacement using Preprocedural CT [Scientific Reports 2024]
Coordinate Ascent Variational Inference for Dirichlet Process Mixtures of Gaussians
List of casual implementations of machine learning models from scratch.
Probabilistic modeling using PyStan with demonstrative case study experiments from Christopher Bishop's Model-based Machine Learning.
[AAAI 2019] "A Probabilistic Derivation of LASSO and L12-Norm Feature Selections", Di Ming, Chris Ding, Feiping Nie
PyTorch implementation of a variational autoencoder (VAE) for use on multi-channel 2D data such as images
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting (KDD 2023)
Material for Philipp Hennig's course: Probabilistic Machine Learning, at Tubingen
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