Teaching

Foundation of Economics Science (Bachelor Course for Computer Scientists)

Course Outline:

  1. Understanding Individual Choices using Machine Learning
  2. Data as a Good
  3. Taxation in the Age of Information Technologies
  4. Growth and Innovation
  5. Discrimination and AI Bias
  6. Networks and Social Media
  7. Blockchain & Bitcoin

Politics and Economics (Master Course)

Course Outline:

  1. Interpersonal Utility Comparison
  2. Politics/Politicians/Ideology
  3. Lobbying
  4. Social Media
  5. Efficiency: Behavioral Biases and Addiction
  6. Efficiency: Market Power
  7. Efficiency: Negative Externalities
  8. Equality: Social Preferences
  9. Equality: Theories of Justice
  10. Equality: Social Insurance
  11. Equality: Discrimination
  12. Conflict

AI Methods in Economics (Bachelor Course)

Course Outline:

  1. Introduction: AI in Economics and Social Sciences
  2. Applications of Supervised Machine Learning
    • Understanding and Predicting Demand
    • Evaluating Workers’ Performance
    • Forecasting Civil Unrest
    • Machine Learning and Causality
  3. Applications of Natural Language Processing
    • Introduction: Natural Language Processing
    • Analysing Central Bank Communications using Topic Models
    • Measuring Knowledge Flows using Text Similarity
    • Evaluating Bias in Text using Text Embeddings
    • Determining Media Slant and Ideologies
    • Quantifying Hate Speech and Free Speech in the Age of Social Media
    • LLM Application in Economics
  4. Applications of Computer Vision
    • Assessing Economic Growth using Night Lights
    • Understanding Image Characteristics Using Computer Vision

Machine Learning Applications in Economics (Bachelor Course for Computer Scientists)

Course Outline:

  1. Introduction Machine Learning and Economics
  2. A Short Introduction to Natural Language Processing
  3. Applications of Supervised Machine Learning
    • Demand Estimation
    • Measuring Racial and Gender Biases Word Embeddings
    • Measuring Visual Bias using Image Classification
  4. Applications of Unsupervised Machine Learning
    • Measuring Ideology using Clustering
    • Measuring Knowledge Transfers with LSA
    • Measuring Central Bank Communication using LDA

Machine Learning in Economics (PhD Course)

Course Outline:

  1. Introduction Machine Learning in Economics
  2. Supervised Machine Learning
  3. Natural Language Processing
  4. Unsupervised Machine Learning
  5. Computer Vision
  6. Deep Learning

Machine Learning Reading Group (PhD Course)

Reading Group Outline:

  1. Text as Data
  2. Supervised Machine Learning
  3. Supervised Machine Learning Classifiers
  4. Causal Machine Learning
  5. Unsupervised Machine Learning
  6. Topic Models

A Primer in Social Media Research (Master Course)

Lecture Outline:

  1. Why Social Media Research is Relevant
  2. How to Collect Social Media Data
  3. Using Machine Learning for Social Media Research
  4. Examples of Social Media Research

Workshop on Effects of Social Media

Workshop Outline:

  1. Lecture 1: Introduction Social Media and Social Media Research
  2. Lecture 2: Fake News and Fact Checking
  3. Lecture 3: Echo Chambers and Polarization
  4. Lecture 4: Social Media and Protests
  5. Lecture 5: Social Media and Elections
  6. Lecture 6: Social Media, Hate, and Content Moderation
  7. Lecture 7: Social Media and Mental Health