M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

A collection of Methods and Models for various architectures of Artificial Neural Networks

Project of IEEE/ACM TASLP “Bayesian Neural Network Language Modeling for Speech Recognition”

Comparison of Several Bayesian Regression Techniques and Gaussian Processes

This is the code for post-processing the shallow shadow tomography data and doing error mitigation on the noisy hardware.

Repository that contains the projects of the Probabilistic Artificial Intelligence class offered in Fall 2021 at ETH Zurich

Colorectal cancer risk mapping through Bayesian Networks

Application of OPAL (Occam Plausibility Algorithm) based Bayesian learning to SEIRD model of COVID-19 disease spread in Texas

Walmart sales prediction with Linear Regression and Random Forests + Bayesian Structure Learning

Exercises for the "Data Analytics" course, University of Bologna (2021/2022)

Research code for continual multilingual ASR and code-switching speech recognition with Whisper, Qwen2-Audio, LoRA, Bayesian low-rank factorization, and weight centralization.

Learning parameters for a bayesian network based on health records data -Assignment 4 (Artificial Intelligence: COL333)

Control architecture for an agent that perceives, plans, learns, and acts in a 3D MMORPG. Evolved from a monolith through reactive rules, state machines, and utility scoring to GOAP planning. JPS/A* pathfinding, DDA line-of-sight, Bayesian adaptation. Pure Python 3.14 (free-threaded), zero dependencies.

computational modelling for restless bandit behavioural data (thalamotomy oedema work)

A fork of a Bayesian learning and inference for state space models library, created due to some dependencies mismatching and for posterity because it's used in a private Colab notebook.

My PDF solutions to our courseworks for the 2024-2025 year

Implementation of FOD-learn (fully observed data), Expectation Maximisation(Partially Observed Data) and Latent Variable Learning in Bayesian Network

Researching policy-induced observability in recommender systems: when serving policies create self-confirming preference models, and when exploration is worth the cost.

Making use of bayesian learning, three common applications are developed.

An introductory course to topics in machine learning studied from a probability theory viewpoint. Covers an overview of basic probability, inference and estimation, regression algorithms, Markov chains, inference for Markov models and the EM algorithm, Markov decision process, and reinforcement learning.