# 參考資料
*大家蒐集到覺得有興趣的文獻
1. Explainable AI:From Theory to Mo7va7on, Applica7ons and Challenges
* https://euads.org/wp-content/uploads/2019/09/xai_Lecture_12.09.2019-Giannotti_2.pdf
2. Limitations of Interpretable Machine Learning Methods
* https://compstat-lmu.github.io/iml_methods_limitations/
3. 手刻ALE
* https://github.com/blent-ai/ALEPython
4. Partial Dependence Plots with PDPbox package
* https://www.kaggle.com/dansbecker/partial-plots
* https://pdpbox.readthedocs.io/en/latest/

### 3/9 倚任副理推薦
- KDD'19 explainable AI 的介紹，有興趣可以看一下喔
[https://www.slideshare.net/KrishnaramKenthapadi/explainable-ai-in-industry-kdd-2019-tutorial?from_action=save](https://www.slideshare.net/KrishnaramKenthapadi/explainable-ai-in-industry-kdd-2019-tutorial?from_action=save)

### 6/3 松憲推薦
- https://arxiv.org/pdf/1909.13584.pdf

###### tags: `參考資料`