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ame

average marginal effects for machine learning

benchmark_2022_counterfactuals

Benchmark code for paper on counterfactuals R Package

code_pitfalls_iml

This repository contains the code for all figures in the paper "General Pitfalls of Model-agnostic Interpretation Methods for Machine Learning Models"

code_relating_pdp_pfi_to_dgp

code_relating_pdp_pfi_to_dgp

constraintmatch

Code for the paper "ConstraintMatch for Semi-constrained Clustering", published at the 2023 International Joint Conference on Neural Networks (IJCNN).

coursetheme

A simple Hugo theme for online courses and lectures with PDFs, Videos and Quizzes.

deeplearninglmu

Python solutions to the Deep Learning course @ LMU (winter term 20/21)

dl4nlp

https://slds-lmu.github.io/dl4nlp/

dummyrepo

eds-test

gadget-reproducibility

grouped_feat_imp_and_effects

hpo_ela

i2dl

https://slds-lmu.github.io/i2dl/

i2ml

https://slds-lmu.github.io/i2ml/

iml-shiny-summary

Shiny Dashboard showing an interpretation summary for any model

iml_methods_limitations

Seminar on Limitations of Interpretable Machine Learning Methods

imlplots

Create Interpretable Machine Learning plots with an interactive Shiny based dashboard

introduction_iml_bliz_summerschool

This repository provides a short introduction into the most popular model-agnostic IML (interpretable machine learning) methods. A presentation containing the theoretical background and examples as well as excercises on real-world data are included.

jtsc4r

Java Time Series Classification code to use in R

latex-math

lecture_advml

lecture_dl4nlp

Repo containing all the lecture material for the dl4nlp course

lecture_i2ml

I2ML lecture repository

lecture_i2ml_learnr_tutorials

lecture_iml

lecture_intro_to_ml_notebooks

Notebooks for the course "Introduction to Machine Learning"

lecture_optimization

lecture_service

Service repo for common infrastructure across all open source lectures

lecture_sl