Machine Learning Assignments

Introduction

This repository contains assignments related to machine learning concepts and algorithms. These assignments were provided by Professor Panos Louridas, and they are designed to help you replicate and analyze studies from various fields using machine learning techniques. Each assignment corresponds to a specific research paper and dataset, focusing on different aspects of social sciences and scientific research.

Dependencies

This project relies on several Python libraries to run. You can find the list of required libraries in the dependencies.txt file. To install these dependencies, simply run the following command:

pip install -r dependencies.txt

This command will install all the necessary libraries listed in the dependencies.txt file. Make sure you have Python and pip installed on your system before running this command.

About dependencies.txt

The dependencies.txt file contains a list of Python libraries along with their versions that are required for this project to run smoothly. Each line in the file represents a separate library. You can add or remove libraries from this file as needed. When running the pip install -r dependencies.txt command, pip will automatically install the specified versions of the libraries listed in the file.

If you need to add or update dependencies, simply edit the dependencies.txt file and re-run the pip install -r dependencies.txt command to ensure that your environment has all the necessary dependencies installed.

Assignments

This repository contains assignments corresponding to specific research papers and datasets. Each assignment focuses on applying machine learning techniques to replicate and analyze studies from various fields. Below are the details of each assignment:

Assignment 1: Economic Connectedness

• Paper: Chetty, R., Jackson, M.O., Kuchler, T. et al. Social capital I: measurement and associations with economic mobility. Nature 608, 108–121 (2022).
• Dataset: Provided by the authors.
• Description: This assignment aims to replicate and analyze the findings of the research paper on social capital and its associations with economic mobility. You will use the provided dataset to apply machine learning algorithms and techniques to explore the relationship between social capital and economic outcomes.

Assignment 2: International Sports Events: Window Dressing and Repression

• Paper: Scharpf, A., Gläßel, C., Pearce, E. (2022) International Sports Events and Repression in Autocracies: Evidence from the 1978 FIFA World Cup, American Political Science Review, 1-18. DOI: 10.1017/S0003055422000958
• Dataset: Harvard Dataverse
• Description: In this assignment, you will replicate and analyze the study on the relationship between repression in autocratic regimes and international sports events. Using the dataset from the Harvard Dataverse, you will apply machine learning techniques to explore the impact of international sports events on repression in autocracies.

Assignment 3: Is Science Becoming Less Disruptive?

• Paper: Nature: Is Science Becoming Less Disruptive?
• Discussion: The Economist, The New York Times, The Atlantic
• Editorial: Nature
• Description: This assignment delves into the study of disruptiveness in science, as discussed in the Nature paper. You will explore the concept of disruptive research and its implications for scientific progress. Utilizing the insights from the paper and the accompanying discussions and editorials, you will analyze trends in scientific research and investigate the factors contributing to the perceived decline in disruptiveness.