TY  - RPRT
        AU  - Askitas, Nikos
        TI  - A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code
        PY  - 2024/May/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 17014
        UR  - https://www.iza.org/index.php/publications/dp17014
        AB  - This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your way up to any other estimator," and apply 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 "feed forward" and "back-propagation", and rounds up with the practical implementation in Python. The objective of this primer is to equip readers with a solid elementary comprehension of first principles and fire some trailblazers to the forefront of AI and causal machine learning.
        KW  - machine learning
KW  - deep learning
KW  - supervised learning
KW  - artificial neural network
KW  - perceptron
KW  - Python
KW  - keras
KW  - tensorflow
KW  - universal approximation theorem
        ER  -