Patrick Altmeyer's repositories
QuartoJulia
Just a small repo to build and host the presentation for my JuliaCon 2022 experience talk on using Quarto with Julia.
endogenous-macrodynamics-in-algorithmic-recourse
Repository for "Endogenous Macrodynamics in Algorithmic Recourse" (Altmeyer et al., 2023)
reinforcement_learning
Contains all code and course work for a module on reinforcement learning.
CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
ConditionalDists.jl
Conditional probability distributions powered by DistributionsAD.jl
dag
A simple module for Directed Acyclical Graphs (DAG). Given a DAG we develop a simple algorithm that identifies all valid adjustment sets.
DegiroAPI
[FORK] An unofficial API for the trading platform Degiro, with the ability to get real time data and historical data
EasyFit.jl
Easy interface for obtaining fits for 2D data
explanations-by-minimizing-uncertainty
Code for "Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties"
fromScratch
A book that collects in a structured manner any ideas, notes, exercises and code that I come across as a student of data science. Work-in-progress.
General
The official registry of general Julia packages
GenerativeModels.jl
Generative Models with trainable conditional distributions in Julia!
intro_dgm
An Introduction to Deep Generative Modeling: Examples
jprobml
Julia code for Probabilistic Machine Learning
JuliaConSubmission.jl
An example package for submissions to JuliaCon
MLJ.jl
A Julia machine learning framework
MLJModelInterface.jl
Lightweight package to interface with MLJ
probai-2022
Materials of the Nordic Probabilistic AI School 2022.
Promises.jl
Use JavaScript Promises syntax in Julia! (alpha)
quarto-example
Collection of minimum working examples related to issues with Quarto
quarto-julia-examples
Examples demonstrating the usage of the pat-alt/quarto-julia extension.
SliceMap.jl
Same-same but different
Turing.jl
Bayesian inference with probabilistic programming.