教學大綱 Syllabus

科目名稱:機器學習與其在行為科學之應用

Course Name: Machine Learning and its Applications in Behavioral Science

修別:選

Type of Credit: Elective

3.0

學分數

Credit(s)

30

預收人數

Number of Students

課程資料Course Details

課程簡介Course Description

「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:

  1. 機器學習之基本概念,包括迴歸(regression)、分類(classification)、交叉效化(cross-validation)、正則化(regularization)、以及優化(optimization)。
  2. 常見之機器學習算則,包括線性方法(linear method)、K最近鄰居(K-nearest neighbors)、決策樹(decision tree)、集成學習(ensemble learning)、以及深度學習(deep learning)等。
  3. 解釋性與因果推論,包括排序特徵重要性(permutation feature importance)、偏相依圖(partial dependence plot)、SHAP(SHapley Additive exPlanations)、以及因果效果(causal effect)之估計。

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。

核心能力分析圖 Core Competence Analysis Chart

能力項目說明


    課程目標與學習成效Course Objectives & Learning Outcomes

    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:

    1. 精熟機器學習的基本概念。
    2. 了解主流機器學習算則。
    3. 解釋學習模型甚至進行因果推論。
    4. 有品味地執行機器學習專案。

    每周課程進度與作業要求 Course Schedule & Requirements

     

    本課程規劃採用16+2之設計,以因應未來台聯大課制之變革。

    週次

    課程內容與指定閱讀

    教學活動/作業

    投入

    總時數

    1

    (2/16)

    課程概論

    Course Overview

    CH1-CH2 in ISLR

    WTOP/PDSH

    Python簡介

    9

    2

    (2/23)

    線性迴歸

    Linear Methods for Regression

    CH3 in ISLR

     

    9

    3

    (3/2)

    scikit-learn簡介

    Introduction to scikit-learn

    sklearn

     

    9

    4

    (3/9)

    正則化與交叉檢驗

    Regularization and Cross-Validation

    CH5-CH6 in ISLR

    繳交HW1

    9

    5

    (3/16)

    線性分類

    Linear Methods for Classification

    CH4 in ISLR

     

    9

    6

    (3/23)

    非線性與可解釋性

    Non-Linearity and Interpretability

    CH3.5 and 4.5 in ISLR

    CH16-CH17 in EMA

    繳交HW2

    9

    7

    (3/30)

    樹為基之方法

    Tree-Based Methods

    CH8 in ISLR

     

    9

    8

    (4/6)

    校際活動週停課

     

    9

    9

    (4/13)

    集成學習與SHAP

    Ensemble Learning and SHAP

    CH8 in ISLR/CH10 in ESL

    SHAP

    繳交HW3

    9

    10

    (4/20)

    期中考

    Midterm

     

    9

    11

    (4/27)

    深度學習I

    Deep Learning I

    CH10 in ISLR

     

    9

    12

    (5/4)

    深度學習II

    Deep Learning II

    CH10 in ISLR

     

    9

    13

    (5/11)

    計畫書口頭報告

    Proposal Oral Presentation

    小組報告

    9

    14

    (5/18)

    因果推論I

    Causal Inference I

    CH4 and CH5 in CIM

     

    9

    15

    (5/25)

    因果推論II

    Causal Inference II

    DoubleML/EconML

     

    9

    16

    (6/1)

    期末報告口頭呈現

    Final Project Oral Presentation

    小組報告

    9

    17

    (6/8)

    自主學習I

    Self-Directed Learning I

    小組投影片繳交

    9

    18

    (6/15)

    自主學習I

    Self-Directed Learning II

     

    9

     

    授課方式Teaching Approach

    70%

    講述 Lecture

    20%

    討論 Discussion

    0%

    小組活動 Group activity

    0%

    數位學習 E-learning

    10%

    其他: Others: 軟體實作

    評量工具與策略、評分標準成效Evaluation Criteria

    1. 課堂參與(5%):含課堂討論與出席。
    2. 作業(30%):共3次,每次佔10%,以資料分析實作為主。
    3. 期中考(30%):以概念之問答題為主。
    4. 期末專案報告(35%,暫定3人一組):含計畫書報告(10%)、期末口頭報告(15%)、投影片書面繳交(10%),根據問題的新奇性與方法應用的邏輯性來評分。

    指定/參考書目Textbook & References

     

    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

    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)

    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)

    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)

    Cunningham, S. (2021). Causal Inference: The Mixtape. (https://www.scunning.com/mixtape.html, CIM)

    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)

    James, G., Witten, D., Hastie, T, & Tibshirani, R. (2021). An introduction to statistical learning: with applications in R (2nd ed). Springer. (https://www.statlearning.com/, ISLR)

    Lundberg, S. M. (2018). SHAP documentation. (https://shap.readthedocs.io/, SHAP)

    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)

    Vanderplas, J. (2016). A Whirlwind Tour of Python. O'Reilly Media, Inc, USA. (https://github.com/jakevdp/WhirlwindTourOfPython, WTOP)

    Vanderplas, J. (2016). Python Data Science Handbook. O'Reilly Media, Inc, USA. (https://jakevdp.github.io/PythonDataScienceHandbook/, PDSH)

     

     

     

    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. (2019). Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems (2nd 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/)

    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)

    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.

    Wickham, H., & Grolemund, G. (2021). R for Data Science. (https://r4ds.had.co.nz/)

    Kuhn, M., & Silge, J. (2021). Tidy Modeling with R. (https://www.tmwr.org/)

     

     

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