Project

Explainable Machine Learning

Model Interpretability and Visualization

Rule

No group and no team. Each person should take one project individually and independently.
Each student should give (a) a proposal, (b) an oral presentation, and (c) a web report.

Requirements

Deadlines and reports
- Proposal : 12/21, Web page.
- Oral Presentation : 01/05, Each person has 10 minutes.
- Web Report : 01/10. reading report and implementation results.
Programming with Python and PyTorch

Readings

Guide to Interpretable Machine Learning - Techniques to dispel the black box myth of deep learning. Towards Data Science, 2020.
Interpretability in Machine Learning, Medium, 2020.
Is Explainable AI (xAI) the Next Step, or Just Hype? 2020.
Mengnan Du, Ninghao Liu, Xia Hu, "Techniques for Interpretable Machine Learning," Communications of the ACM, Vol. 63 No. 1, Pages 68-77, January 2020.
Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in Python, 2019.
Python Libraries for Interpretable Machine Learning, 2019.
The great AI debate: Interpretability, Medium, 2019.
人工智慧新發展，可向人類解釋思考過程，2018.
MIT Lincoln Laboratory develops AI that shows its decision-making process, 2018
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson, "Understanding Neural Networks Through Deep Visualization," ICML 2015.
WELCOME TO PYTORCH TUTORIALS, PyTorch Web Site

Two Topics

Machine Learning Visualization, CNN Visualization

ML VISUALIZATION

Generate Publication-Ready Plots Using Seaborn Library, 2020/12.
- Part-1. Different types of plots using seaborn
- Part-2. Facet, Pair and Joint plots using seaborn
- Part-3. Seaborn’s style guide and colour palettes
- Part-4. Seaborn plot modifications (legend, tick, and axis labels etc.)
- Part-5. Plot saving and miscellaneous
Visdom (GitHub) [PyTorch], Facebook Research
YellowBrick, ELI5, LIME, MLxtend.
Machine Learning Visualization, 2020.
Python Libraries for Interpretable Machine Learning, 2019.
LIME vs. SHAP: Which is Better for Explaining Machine Learning Models? 2020/12

CNN VISUALIZATION

Explainable MNIST classification, 2020.
- Part 1 : dissection of a ConvNet. Explaining the decisions of a ConvNet in classifying MNIST images
- Part 2: building a ConvNet by hand (Not Yet Completed)
Papers about interpretable CNN (GitHub)
Awesome Interpretable Machine Learning (GitHub)
GWU_data_mining/10_model_interpretability (GitHub)

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