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

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1. Login to https://vse.syzygy.ca/ with your CWL to ensure you can access our JupyterHub
2. Click Here to install the QuantEcon Julia Lectures there
   • Later you will need to do a local installation by following the Getting Started but this is a better way to begin
   • For support with vse.syzygy.ca, email [email protected]

Most of the course will be taught using Julia, but we will briefly introduce Python (or R) for discussing "medium" and "big" data pipeline.

See Syllabus for more details

Problem sets should be submitted as a single Jupyter notebook on Canvas, with the code and output clean.

• Problem Set 1
  • Exercises 7, 8(a), and 8(b) in Introductory Examples
  • Redo any exercise of your choice from 1 to 6 in Introductory Examples
    • In all "Redo" look at the provided solutions afterwards, but still submit your code
  • Redo Exercise 5 in Julia Essentials except
    • Pass in a range instead of a, b, and n, e.g. nodes = -1.0:0.5:1.0
    • Hint: try step(nodes), length(nodes), nodes[1] and nodes[end]
    • Also, instead of the while in the solution, find a better way to efficiently bracket the x in the nodes. Hint: Type ?÷ into jupyter to explore quotients from Euclidean division

1. September 4th: Introduction to Julia

   • Julia Environment, Introductory Examples, and Julia Essentials

2. September 9th:

3. September 11th:

   • Problem Set 1 Due

4. September 16th:

5. September 18th:

6. September 23th:

7. September 25th:

8. September 30th:

9. October 2nd:

10. October 7th:

11. October 9th:

12. October 14th: NO CLASS (Thanksgiving)

13. October 16th:

14. October 21th: (Paul takes over)

15. October 23th:

16. October 28th:

17. October 30th:

18. November 4th:

19. November 6th:

20. November 11th: NO CLASS (Remembrance Day)

21. November 13th:

22. November 18th:

23. November 20th:

24. November 25th:

25. November 27th:

26. Final Project due mid December