ECON622 - Fall 2020

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

Course materials

• Get a GitHub ID and apply for the Student Developer Pack to get further free features
• Consider clicking Watch at the top of this repository to see file changes
• (Optionally) installing GitHub Desktop for easy downloads/updates of materials

Syllabus

See Syllabus for more details

Problem Sets

See problemsets.md.

Lectures

1. September 9th: Environment and Introduction to Julia

   • Julia Environment, Introductory Examples

2. September 14th: Introduction and Variations on Fixed-points

   • Introductory Examples

3. September 16th: Introduction to types

   • Self-study: Julia Essentials
   • Self-study: Fundamental Types
   • Start Intro to Generic Programming
   • Flip through the Matlab - Julia Cheat Sheet

4. September 21st

   • Self-study: Intro to Generic Programming
   • Self-study: Generic Programming

5. September 23rd

   • Generic Programming
   • Self-study: General Packages
   • Self-study: Data and Statistical Packages
   • Notes on Quadrature applying generic programming

6. September 28th

   • Self-study: Linear Algebra
   • Self-study: Orthogonal Projections
   • Notes on Numerical Linear Algebra applying generic programming

7. September 30th

   • The Schur Decomposition
   • Desktop Tools/Package Management
   • Git and Github

8. October 5th:

   • Finite Markov Chains

9. October 7th

   • Continuous Time Markov Chains
   • Self-study Discrete State Dynamic Programming

10. October 12th: Thanksgiving

11. October 14th

    • Testing and Packages
    • Conditioning and Numerical Stability

12. October 19th

    • Iterative Methods
    • Intro to AD
    • Self-study: https://github.com/ubcecon/cluster_tools for instructions on setting up/using the cluster.

13. October 21st

    • Optimization algorithms
    • Optimization packages

14. October 26th

    • Extremum estimation and inference

15. October 28th

    • Empirical likelihood

16. November 2nd

    • Bootstrap

17. November 4th:

    • Coding for performance
    • Self-study: Need for speed

18. November 9th

    • GPU usage

19. November 11th: Remembrance Day

20. November 16th

    • Machine learning or older version with slides

21. November 18th

    • Machine learning in Julia

22. November 23rd

    • Neural Networks

23. November 25th

    • Convolutional and Recurrent Neural Networks

24. November 30th

    • Project proposal due

25. December 2nd

26. December 18th

    • Final Project due December 18th