The Riemannian Motion Policy robot control framework is implemented from scratch. Obstacle avoidance is tested in cluttered environments.

My solutions to the assignments in the book: "A Student’s Guide to Bayesian Statistics" by Ben Lambert.

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

A tutorial on Hierarchical Bayesian Modelling

The Good Research Code Handbook

Data and stimuli for "Learning physical parameters from dynamic scenes"

A curriculum for learning about foundation models, from scratch to the frontier

Python best practices guidebook, written for humans.

Lecture slides and scripts (LaTeX sources) in AI, Robotics, Machine Learning, Maths, Optimization

Interactive textbook on state-space models

State Space Models library in JAX

Friends don't let friends make certain types of data visualization - What are they and why are they bad.

Gaussian Mixture Regression

This library provides expression trees for representation of geometric expressions and automatic differentiation of these expressions. This enables to write down geometric expressions at the positi…

This repository holds the code for the NeurIPS 2022 paper, Semantic Probabilistic Layers

🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps

ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

Apps (mostly streamlit) for the "Probabilistic Machine Learning" Lecture course at the University of Tübingen

Material for the "Probabilistic Machine Learning" Course at the University of Tübingen, Summer Term 2023

Probabilistic Circuits from the Juice library

Python programs, usually short, of considerable difficulty, to perfect particular skills.

This is a companion to the ‘Mathematical Foundations’ section of the book, Mathematics for Machine Learning by Marc Deisenroth, Aldo Faisal and Cheng Ong, written in python for Jupyter Notebook.

Implementation of Judea Pearl's "Multi-stage Simpson's Paradox Machine".

Machine Learning and Causal Inference taught by Brigham Frandsen

A toolkit for auto-generation of OpenAI Gym environments from RDDL description files.

Python implementation of Bayesian Program Learning tools (with PyTorch)