https://github.com/quantecon/melbourne_2024
https://github.com/quantecon/melbourne_2024
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/quantecon/melbourne_2024
- Owner: QuantEcon
- Created: 2024-05-20T23:06:45.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-15T02:59:08.000Z (almost 2 years ago)
- Last Synced: 2025-04-20T01:21:47.157Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 16.8 MB
- Stars: 4
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
----
# Computional Economics Workshop
## University of Melbourne
This is the homepage for the Computational Economics
Workshop to be held at the University of Melbourne on August 15th 2024.
## Abstract
Several new open source scientific computing environments have appeared in
recent years, generated by demand from and investment in artificial intelligence
and related fields. Economists can greatly enhance their modeling and data
processing capabilities by exploiting these new computational tools. This
workshop will provide a brief overview of core topics, including automatic
differentiation, parallel computing, and just-in-time compilers. We will discuss
how these tools can be applied to a range of economic applications.
All attendees should bring a laptop computer with access to the internet.
The workshop is suitable for people with interest in computational work but no
programming experience is required.
## Lead Instructor
The lead instructor for the workshop will be [John Stachurski](https://johnstachurski.net/) (Australian National University).
## Guest Instructors
The workshop will include a session titled Computational Economics in Action, with lectures by [James Hansen](https://sites.google.com/site/jamesfrhansen/home) and [Yong Song](https://sites.google.com/view/ysong1).
## Times and Date
* Date: August 15th 2024
### Location
* Room 404, FBE Building 111 Barry St, Carlton
## Topics
* An overview of modern scientific computing
* AI and its impact on economic modeling
* Quick introduction to Python
* Accelerating Python using Numba and Fortran
* Automatic differentiation
* Introduction to JAX and GPU computing
* Gini coefficients and Lorenz curves