Type of Credit: Elective
Credit(s)
Number of Students
「機器學習與其在行為科學之應用」乃為行為科學領域學生所設計之機器學習課程，除機器學習的基礎理論外，強調於其在行為科學之應用，以及機器學習模型之可解釋性與其如何協助因果推論。
本課程內容涵蓋以下三面向：
此外，本課程將使用 Python 的scikit-learn、PyTorch等開源軟體，以進行前述方法於行為資料之實作。由於授課地點為一般教室，故建議修課學生準備筆記型電腦，以方便課堂中即時操作回饋。
能力項目說明
本課程假設學生具有基礎統計學之知識，以及願意學習Python程式設計之熱忱。盼望透過課程講授與實作，修課學生能夠具備以下之能力：
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
週次 |
課程內容與指定閱讀 |
教學活動/作業 |
投入 總時數 |
|
1 (2/22) |
課程概論 Course Overview CH1-CH2 in ISLP |
|
9 |
|
2 (2/29) |
Python簡介 WTOP/PDSH |
lab_python |
9 |
|
3 (3/7) |
線性迴歸 Linear Methods for Regression CH3 in ISLP |
lab_regression |
9 |
|
4 (3/14) |
scikit-learn簡介 Introduction to scikit-learn sklearn |
lab_sklearn |
9 |
|
5 (3/21) |
正則化與交叉檢驗 Regularization and Cross-Validation CH5-CH6 in ISLP |
lab_reg_cv 繳交HW1 |
9 |
|
6 (3/28) |
線性分類 Linear Methods for Classification CH4 in ISLP |
lab_classification |
9 |
|
7 (4/4) |
校際活動週停課 |
|
9 |
|
8 (4/11) |
非線性與可解釋性 Non-Linearity and Interpretability CH3.5 and 4.5 in ISLP CH8, CH16, CH17 in EMA |
lab_nl_int 繳交HW2 |
9 |
|
9 (4/18) |
樹為基之方法 Tree-Based Methods CH8 in ISLP SHAP |
lab_tree |
9 |
|
10 (4/25) |
深度學習 Deep Learning CH10 in ISLP SHAP |
lab_dl |
9 |
|
11 (5/2) |
集成學習 Ensemble Learning CH8 in ISLP/CH10 in ESL |
lab_ensemble 繳交HW3 |
9 |
|
12 (5/9) |
期中考 Midterm |
|
9 |
|
13 (5/16) |
進階深度學習 I Advanced Deep Learning I |
小組計畫書報告 |
9 |
|
14 (5/23) |
進階深度學習 II Advanced Deep Learning II |
|
9 |
|
15 (5/30) |
因果推論 Causal Inference CH4 and CH5 in CIM DoubleML/EconML |
|
9 |
|
16 (6/6) |
期末報告口頭呈現 Final Project Oral Presentation |
小組期末報告 |
9 |
|
17 (6/8) |
自主學習 Self-Directed Learning |
小組投影片繳交 |
9 |
|
18 (6/15) |
自主學習 Self-Directed Learning |
|
9 |
本課程所使用之指定教材，其作者皆已於網路提供了免費之內容，而多數之參考資料亦可透過合法管道取得。
指定書目
James, G., Witten, D., Hastie, T, & Tibshirani, R., Taylor, J. (2023). An introduction to statistical learning: with applications in Python. Springer. (https://www.statlearning.com/, ISLP) (為此課程最主要的教科書)
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed). Springer. (https://web.stanford.edu/~hastie/ElemStatLearn/, ESL) (為ISLP的進階版本，著重在理論)
Vanderplas, J. (2016). A Whirlwind Tour of Python. O'Reilly Media, Inc, USA. (https://github.com/jakevdp/WhirlwindTourOfPython, WTOP) (python教學使用)
Vanderplas, J. (2016). Python Data Science Handbook. O'Reilly Media, Inc, USA. (https://jakevdp.github.io/PythonDataScienceHandbook/, PDSH) (python教學使用)
Cunningham, S. (2021). Causal Inference: The Mixtape. (https://www.scunning.com/mixtape.html, CIM) (介紹因果推論時使用)
Biecek, P., & Burzykowski, T. (2020). Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python. (https://ema.drwhy.ai/, EMA) (模型解釋性使用)
Lundberg, S. M. (2018). SHAP documentation. (https://shap.readthedocs.io/, SHAP) (套件文件)
Bach, P., Chernozhukov, V., Kurz, M. S., and Spindler, M. (2022), DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python. Journal of Machine Learning Research, 23(53), 1-6. (https://docs.doubleml.org/, DoubleML) (套件文件)
Battocchi, K., Dillon, E., Hei, M., Lewis, G., Oka, P., Oprescu, M., Syrgkanis, V. (2019). EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. (https://econml.azurewebsites.net/, EconML) (套件文件)
Pedregosa, F., Varoquaux, Ga"el, Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. (https://scikit-learn.org/, sklearn) (套件文件)
參考書目
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018), Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21, 1-68.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (3rd ed.). O’Reilly. (因此書未提供免費閱讀管道，故未列入指定教材，但極力推薦)
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer. (http://appliedpredictivemodeling.com/)
Kuhn, M., & Johnson, K. (2020). Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press. (http://www.feat.engineering/)
Kuhn, M., & Silge, J. (2021). Tidy Modeling with R. (https://www.tmwr.org/)
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 4768–4777.
Lundberg, S. M., Erion, G., Chen, H. et al. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67.
Molnar, C. (2021). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. (https://christophm.github.io/interpretable-ml-book/index.html)
Stevens, E., Antiga, L., & Viehmann, T. (2020). Deep Learning with PyTorch. (https://www.manning.com/books/deep-learning-with-pytorch)
James, D. (2018). Introduction to Machine Learning with Python: A Guide for Beginners in Data Science (1st. ed.). (因此書未提供免費閱讀管道，故未列入指定教材，但極力推薦，此書相當適合初學者)
Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests, Journal of the American Statistical Association, 113(523), 1228-1242.
| 書名 Book Title | 作者 Author | 出版年 Publish Year | 出版者 Publisher | ISBN | 館藏來源* | 備註 Note |
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