Machine Learning for Economists: An Introduction

Machine Learning (henceforth ML) refers to the set of algorithms and computational methods which enable computers to learn patterns from training data without being explicitly programmed to do so. ML uses training data to learn patterns by estimating a mathematical model and making predictions in out of sample based on new or unseen input data. ML has the tremendous capacity to discover complex, flexible and crucially generalisable structure in training data. Conceptually speaking, ML can be thought of as a set of complex function approximation techniques which help us learn the unknown and potentially highly nonlinear mapping between the data and prediction outcomes, outperforming traditional techniques. 1 In this exposition, my aim is to provide a basic and non-technical overview of 2 machine learning and its applications for economists including development economists. For more technical and complete treatments, you may consult Alpaydin (2020) and James, et al. (2013). You may also wish to refer to my four lecture series on machine learning on YouTube https:// www.youtube.com/watch?v=E9dLEAZW3L4 and my GitHub page for detailed and more technical lecture slides https://github.com/sonanmemon/Introductionto-ML-For-Economists

Earth Observation Image Semantics: Latent Dirichlet Allocation Based Information Discovery

Land cover maps are among the most important products of Remote Sensing (RS) imagery. Despite remarkable advancements in land cover classification techniques, abundant detailed information in the very high-resolution RS images necessitates further improvements to harness the data and discover detailed semantic information. Moreover, scarcity of the labelled data and its quality is a major limitation in RS land cover mapping. In the present study, Latent Dirichlet Allocation is employed for semantic discovery in RS images and a novel kernel-based Bag of Visual Words model is proposed for land cover mapping