跳到主要內容區塊

國立臺灣大學公共事務研究所

行政公告

【行政公告】「丈量社會:運用因果機器學習技術」3/14(四)開課,敬請留意!
  • 發布單位:公共事務研究所

本所選修「丈量社會:運用因果機器學習技術」上課時間如下

敬請留意開課日期為3/14(四)。

Date

日期

Subject

主題

3/14

Week 1: Introduction to machine learning I

Machine Learning Applications

What is Machine Learning?

The Life Cycle of Machine Learning Projects

  • scoping
  • data preparation
  • modeling and evaluation
  • serving
  • monitoring

Data Science Roadmaps & Learning Resources

3/21

Week 2: Introduction to Machine Learning II

Model Training Basics

  • Bias-variance Trade-off
  • Train-test Split
  • Hyperparameter Tuning
  • Model Validation

The Components of Machine Learning

  • Machine Learning Algorithm
  • Learning Objective
  • Optimization Strategies
  • Performance Metrics

3/28

Week 3: Introduction to Machine Learning III and Causal Inference 101

Hyperparameter Optimization

  • Grid Search
  • Randomized Search
  • Bayesian Approach

Model Explainability

  • Feature Importance
  • Shapley Value and SHAP

Potential Outcome Model

  • ATE, ATT and Bias
  • Key Assumptions for Causal Inference from
    • Experimental data
    • observational data
  • Confoundedness, Self-selection, and Multicollinearity

4/11

Week 4:  Guest Lecture: Principles of Data Visualization

 

By Multinational Company Client Training Team Data Analyst, Victoria Yang

4/18

Week 5: Guest Lecture: For More Efficient Experiments: Variance Reduction, Quantile Treatment Effect and Bootstrap

By Realtor.com Senior Data Scientist, Ying-Kai Huang

4/25

Week 6: Tackling Measured / Unmeasured Confounding

  • Matching Methods
  • Instrument Variable, IV Forest
  • Double Machine Learning

5/2

Week 7: Causal Mediation: Seeking Mechanism

  • Total, Direct, and Indirect Effects

5/9

Week 8: Targeting the Right People

  • Honest Causal Trees/Forest
  • Heterogeneous Treatment Effects