Notes

These are my notes on computational economics and modern machine learning methods. The goal is to eventually turn them into a graduate-level course on ML and computational economics. All code and notebooks are available on GitHub.

---

Deep Learning

1. Deep Learning and Smooth Interpolation
Application: Runge’s Phenomenon
Notebook

---

2. Linear-Quadratic Dynamic Programming
Application: A firm with quadratic adjustment cost facing a linear demand
Notebook

---

3. Learning Non-Smooth Functions
Application: McCall Search Model
Notebook

---

4. Derivative of a Neural Network with Respect to the Input
Application: Solving a simple ordinary differential equation
Notebook

---

Optimization

1. On the Magnitude of the Learning Rate and the Stability of Gradient Descent
Application: Solving linear regression using gradient descent
Notebook