Course Overview:
This advanced online course is designed for academics, PhD students, and professors interested in integrating machine learning techniques into economic research and analysis. The course covers foundational principles, practical applications, and cutting-edge techniques of machine learning in the context of economics.
Course Objectives:
Understand the role of machine learning in modern economic research.
Develop practical skills in applying machine learning algorithms to economic data.
Implement machine learning models using tools such as Python and R.
Analyze and interpret results from machine learning models to inform economic policy and decision-making.
Target Audience:
Academics in economics and related fields.
PhD students focusing on econometrics, data science, and computational economics.
Professors and researchers seeking to enhance their methodological toolkit with machine learning.
Course Structure: The course is divided into six modules, each featuring lectures, readings, assignments, and hands-on exercises. Participants will engage with video lectures, interactive coding sessions, and peer discussions.
Module 1: Introduction to Machine Learning in Economics
Week 1: Overview of Machine Learning
What is machine learning? Key concepts and terminology.
Applications of machine learning in economics and social sciences.
Week 2: Machine Learning Basics
Supervised vs. unsupervised learning.
Common algorithms: regression, classification, clustering.
Readings:
"Pattern Recognition and Machine Learning" by Christopher M. Bishop.
"Machine Learning Yearning" by Andrew Ng (available online).
Module 2: Data Preparation and Exploration
Week 3: Data Cleaning and Preprocessing
Handling missing data, outliers, and normalization.
Feature selection and engineering.
Week 4: Exploratory Data Analysis (EDA)
Techniques for visualizing and summarizing economic data.
Using EDA to inform model selection.
Readings:
"Data Science for Economists" by James W. Stock and Mark W. Watson.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
Module 3: Supervised Learning Techniques
Week 5: Linear and Logistic Regression
Implementing and interpreting linear and logistic regression models.
Regularization techniques (Lasso, Ridge).
Week 6: Advanced Supervised Learning
Decision trees, random forests, and gradient boosting machines.
Model evaluation metrics: ROC curves, AUC, confusion matrices.
Readings:
"Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
"The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Module 4: Unsupervised Learning and Dimensionality Reduction
Week 7: Clustering Techniques
K-means clustering, hierarchical clustering.
Applications of clustering in economic data analysis.
Week 8: Dimensionality Reduction
Principal Component Analysis (PCA) and t-SNE.
Reducing dimensionality for better model performance and visualization.
Readings:
"Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy.
Selected articles from the Journal of Econometrics.
Module 5: Time Series Analysis with Machine Learning
Week 9: Machine Learning for Time Series Forecasting
Techniques for forecasting economic time series data using machine learning.
Feature engineering for time series analysis.
Week 10: Advanced Time Series Models
Long Short-Term Memory (LSTM) networks and other deep learning models for time series.
Model evaluation and performance metrics for time series.
Readings:
"Deep Learning for Time Series Forecasting" by Jason Brownlee.
"Machine Learning for Time Series Forecasting with Python" by Francesca Lazzeri.
Module 6: Applications and Case Studies
Week 11: Real-World Economic Applications
Case studies on applying machine learning to economic policy analysis, financial markets, and labor economics.
Guest lectures from leading experts in machine learning and economics.
Week 12: Capstone Project and Presentations
Participants develop a machine learning project on an economic topic of their choice.
Presentation of projects and peer review.
Readings:
Recent articles from the Journal of Economic Perspectives and the Review of Economics and Statistics.
Relevant research papers and case studies provided throughout the course.
Assessment and Certification:
Assignments:
Weekly coding exercises and practical applications.
Mid-term project involving a supervised or unsupervised learning model.
Final Project:
Comprehensive machine learning project applying techniques learned to a real-world economic dataset.
Presentation and peer feedback.
Certification:
Participants who complete all modules, assignments, and the final project will receive a certificate of completion.
Course Delivery:
The course will be delivered through a combination of pre-recorded video lectures, live coding sessions, interactive tutorials, and discussion forums.
All course materials, including code samples, datasets, and lecture slides, will be available online.
Instructor:
The course will be led by a team of experienced data scientists and economists with extensive expertise in machine learning and econometrics.
Enrollment:
Participants can enroll through the university’s online learning platform.
Enrollment will be open to individuals with a foundational knowledge of econometrics and programming.
By the end of this course, participants will have a robust understanding of how to apply machine learning techniques to economic research, equipped with the skills to conduct innovative analysis and inform policy decisions.