Advanced Quantitative Research

Course Materials

01 Introduction
02 Randomized Trials
03 Instrumental Variables
04 Regression Discontinuity
05 DiD 1: Standard DiD
06 DiD 2: Synthetic Control
07 DiD 3: Time Variant
08 ML 1: Intro to Machine Learning
09 ML 2: Classification
10 ML 3: Regression
11 Factor Analysis
12 ML 4: Unsupervised
13 ML 5: Intro to NLP

Students are encouraged to read and watch the items under Before the Class
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Important Optional

# Module 10

Supervised ML: Regression

Learning Objectives: Bagging and boosting models for regression problems; understanding the concept of overfitting and underfitting; learning to use hyperparameter optimization techniques

Before the Class

Prepare for Quiz

Here's the question bank for Quiz 2 (October 31, 2024).

Readings

Hal Daumé III, A Course in Machine Learning. Chapter 7. Linear Models & Chapter 8. Bias and Fairness

Videos

StatQuest, "A Gentle Introduction to Machine Learning"
StatQuest, "Bias and Variance"
"Underfitting & Overfitting - Explained"

During the Class

Kim, Serena Y., Koushik Ganesan, Crystal Soderman, and Raven O’Rourke. "Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network." EPJ Data Science 12, no. 1 (2023): 25.