Data Science and Machine Learning

These are recitations for my Introduction to Machine Learning for Economists alongside code and slides. My hope is to make it easier for economists to delve into machine learning topics. Recitation Code Repository

Lecture 1: Introduction to Machine Learning for Economists: Intro to Course, Intro to Python, Intro to Pandas

Here is the introductory recitation in machine learing. I cover an introduction to Python and Pandas and review math for machine learning:

Intro to Course, math review

Intro to Python and Pandas

Lecture 2: Introduction to Machine Learning for Economists: Intro to linear algebra, linear regression, Numpy, and optimization

This recitation covers basic linear algebra techniques for machine learning alongside linear regression and regularization including LASSO and Ridge, optimization in machine learning, and Numpy.

Intro to Linear Algebra, linear regression, Numpy, and Optimization

Lecture 3: Introduction to Machine Learning for Economists: Dimensionality Reduction Techniques and Model Validation

This recitation covers basic Dimensionality Reduction Techniques and Model Validation. I discuss and walk through code on PCA, LDA, and provide thoughts on cross-validation and avoiding overfitting.

Dimensionality Reduction Techniques and Model Validation

Lecture 4: Introduction to Machine Learning for Economists: Logistic Regression, Neural Networks, Pytorch, and Huggingface

This recitation covers Logistic Regression, Neural Networks, Pytorch, and Huggingface. I show talk about recent advancements in neural networks and how they relate to economics.

Logistic Regression, Neural Networks, Pytorch, and Huggingface

Lecture 5: Introduction to Machine Learning for Economists: Support Vector Machines, Tree-based Models, Random Forests, and Sklearn

This recitation covers decision trees, random forests, Sklearn, and Boosting methods including Adaboost. I show to implement algorithms from scratch and in Sklearn.

Support Vector Machines, Tree-based Models, Random Forests Sklearn

Lecture 6: Unsupervised Learning, Reinforcment Learning, SQL, and Data Sources

This recitation covers SQL, reinforcement learning, K-means, K-mediods, and where to find data:

Unsupervised Learning, Reinforcment Learning, SQL, Data Sources