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.
Proposal : 12/21, Web page.
Oral Presentation : 01/05, Each person has 10 minutes.
Web Report : 01/10. reading report and implementation results.
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.
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.
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.
Generate Publication-Ready Plots Using Seaborn Library, 2020/12.
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
LIME vs. SHAP: Which is Better for Explaining Machine Learning Models? 2020/12
Explainable MNIST classification, 2020.
Papers about interpretable CNN (GitHub)
GWU_data_mining/10_model_interpretability (GitHub)