slds-lmu/lecture_i2ml

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slds-lmu/lecture_i2ml

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I2ML lecture repository

Introduction to Machine Learning (I2ML)

This Project offers a free, open source introductory and applied overview of supervised machine learning.

Main course site: https://slds-lmu.github.io/i2ml/

Contents, License, Team and Further Info

Please see the main course site.

Future Development

We are collecting ideas for future development in this list. For suggestions regarding the existing material, please open an issue.

Help is appreciated and welcome!

We hope to continuously improve and expand this course over the coming years. We strongly believe in open source and collaborative work. Please contact us if you think likewise and would like to contribute. See also our contributing guidelines

Are you an ML expert and like the course, but have some feedback or consider extending it? Write an email to Bernd and Fabian (see Team page) or Open an issue.
Are you a student taking the lecture - either at the LMU or online - and you spotted a typo, think we should rephrase something be or even would like to provide a new quiz question or coding example? Please consider providing a pull request. To do so, please check out the devel branch of the repo and add your fixes there. Writing an e-mail or opening an issue with suggested improvements is obviously very welcome as well!
You are none of the above but would like to contribute, get in touch / open issues / create pull requests! We are happy about any help.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

We would appreciate if you contact us in case you are re-using our course. Knowing this helps us to keep the project alive. Thank you!

Further topics

We also have a list of unfinished topics that could be again interesting in the future: Link to G-Drive

seminar_multimodal_dl

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

iml_methods_limitations

Seminar on Limitations of Interpretable Machine Learning Methods

yahpo_gym

Surrogate benchmarks for HPO problems

Jupyter Notebook

imlplots

Create Interpretable Machine Learning plots with an interactive Shiny based dashboard

lecture_dl4nlp

Repo containing all the lecture material for the dl4nlp course

paper_2019_iml_measures

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition

lecture_iml

Jupyter Notebook

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"

i2ml

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

seminar_nlp_ss20

Link to website

lecture_optimization

tsclassification

Wrapping the Time-Series Classification Java Implementations for R

mosmafs

Multi-Objective Simultaneous Model and Feature Selection

latex-math

qdo_yahpo

A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models

lecture_i2ml_learnr_tutorials

lrz_configs

yahpo_data

Data required for slds-lmu/yahpo_gym

paper_2021_categorical_feature_encodings

hpo_ela

paper_2023_survival_benchmark

Benchmark for Burk et al. (2024)

iml-shiny-summary

Shiny Dashboard showing an interpretation summary for any model

i2dl

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

wildlife-ml

paper_2021_xautoml

Jupyter Notebook

lecture_sl

vistool

wildlife-experiments

Jupyter Notebook

seminar_website_skeleton

jTSC4R

Java Time Series Classification code to use in R

paper_2021_multi_fidelity_surrogates

Surrogate benchmarks for HPO problems

surrogates

grouped_feat_imp_and_effects

dl4nlp

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

rcourses_notebook_deeplearning

Jupyter Notebook

paper_2019_multiobjective_rfms

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

Jupyter Notebook

lecture_advml

yahpo_exps

Experiments for yahpo gym

lecture_template

rcourses_notebook_clustering

Jupyter Notebook

paper_2019_variationalResampleDistributionShift

Variational Resampling Based Assessment of Deep Neural Networks Robustness under Distribution Shift

mlw-htr

Jupyter Notebook

ame

average marginal effects for machine learning

qdo_nas

paper_2024_rpid

benchmark_2022_counterfactuals

Benchmark code for paper on counterfactuals R Package

phd_thesis_dummy_template

rcourses_notebook_ml

Jupyter Notebook

mbt_comparison

mobafeas

Model Based Feature Selection

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.

lecture_i2dl

Introduction to Deep Leaning

Jupyter Notebook

lecture_service

Service repo for common infrastructure across all open source lectures

paper_2023_eagga

Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

paper_2023_regression_suite