A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code

The paper and its code aim at enabling social scientists, who wish to do so, to add Machine Learning techniques to their research toolkit, by adopting a pedagogical strategy inspired by the adage "once you understand OLS, you can work your way up to any other estimator," and applying it to Machine Learning.

Focusing on a single-hidden-layer artificial neural network, the paper discusses its mathematical underpinnings, including the pivotal Universal Approximation Theorem—an essential "existence theorem". The exposition extends to the algorithmic exploration of solutions, specifically through feedforward and backpropagation, and rounds up with the practical implementation in Python. The objective of this primer is to equip readers with a solid foundational comprehension and jump start their journey to the forefront of AI and causal machine learning.