richardgu26 / ECON622

ECON622

This is a graduate topics course in computational economics, with applications in datascience and machine learning.

Course materials

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Syllabus

See Syllabus for more details

Problem Sets

See problemsets.md.

Lectures

1. September 6: Environment and Introduction to Julia
   • Julia Environment, Introductory Examples
2. September 8: Introduction and Variations on Fixed-points
   • Introductory Examples
3. September 13: Introduction to types
   • Self-study: Julia Essentials
   • Self-study: Fundamental Types
   • Note on broadcasting
   • Intro to Types and Generic Programming
4. September 15
   • Generic Programming
5. Sepember 20
   • Generic Programming
   • Self-study: General Packages
   • Self-study: Data and Statistical Packages
   • Notes on Quadrature applying generic programming
6. September 22
   • Self-study: Linear Algebra
   • Self-study: Orthogonal Projections
   • Notes on Numerical Linear Algebra applying generic programming
   • Iterative Methods
7. September 27
   • Conditioning and Numerical Stability
   • Desktop Tools/Package Management
   • Git and Github
8. September 29:
   • Discrete State Dynamic Programming
9. October 4
   • Testing and Packages
10. October 6
    • Intro to AD and Differentiation for Hackers
11. October 11
    • Self-study: https://github.com/ubcecon/cluster_tools for instructions on setting up/using the cluster.
12. October 13
    • Optimization algorithms
    • Optimization packages
13. October 18
    • Extremum estimation and inference
14. October 20
    • Empirical likelihood
    • Bootstrap
15. October 25
    • Machine learning or older version with slides
    • Machine learning in Julia
16. October 27
    • Neural Networks
17. November 1:
    • Convolutional and Recurrent Neural Networks
18. November 3
    • Transformers
19. November 8
    • Coding for performance
    • Self-study: Need for speed
    • GPU usage
20. November 10
    • Bayesian Methods
21. November 15
    • ODE
22. November 17
    • Stochastic ODE
23. November 22
    • Gradient Boosting
24. November 24
25. November 29 Dynamic discrete choice
26. December 1
    • Project proposal due
27. Decemeber 16
    • Final Project due

Look under "Releases" for earlier versions of the course.