|
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
|