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
Use the icons below to determine whether a reading is important or optional:

Important Optional

# Module 9

Supervised ML: Classification

Learning Objectives: Understand the differences between supervised and unsupervised learning; Implement a random forest model for classification; Evaluate the model performance properly using F1, recall, precision, and accuracy

Before the Class

Readings

Jeremy Watt, Reza Borhani, & Aggelos Katsaggelos. "Machine learning refined : foundations, algorithms, and applications." Chapter 4. Classification

Videos

CrashCourse, "Supervised Learning: Crash Course AI #2"
"Precision vs Recall"
StatQuest, "Machine Learning Fundamentals: The Confusion Matrixg" (Watch it 0:30 - 4:50)
StatQuest, "A Gentle Introduction to Machine Learning" Optional

Course Materials

Students are encouraged to read and watch the items under Before the Class Use the icons below to determine whether a reading is important or optional:

Supervised ML: Classification

Readings

Videos

Students are encouraged to read and watch the items under Before the Class
Use the icons below to determine whether a reading is important or optional: