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Scientific Computing in Finance

Math-GA 2048, New York University, Spring 2016

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Course Materials

All lecture slides are generated using IPython notebook, click the link ipynb to view the source IPython notebook. You can also download all the source ipython notebooks and related Python codes from http://github.com/yadongli/nyumath2048

Lecture slides and homework will be published weekly before the Wednesday lecture.

Old lecture slides from Spring 2015 semester are available here

Lecture slides are in HTML format, which requires javascript enabled browser to render properly.

Lecture Materials Instructor
1. Introduction lecture slides (ipynb), homework @yadongli
2. Linear Algebra I lecture slides (ipynb), homework @yadongli
3. Linear Algebra II lecture slides (ipynb), homework @yadongli
4. Rootfinding & Interpolation lecture slides (ipynb), homework @yadongli
5. Deltas and Hedging lecture slides (ipynb), homework Ariye Shater, @yadongli
6. Optimization lecture slides (ipynb), homework @hcheng66
7. Linear Programming lecture slides (ipynb), homework @hcheng66
8. Monte Carlo lecture slides (ipynb), homework @yadongli
9. Variance Reduction lecture slides (ipynb), homework @yadongli
10. Entropy & Allocation lecture slides (ipynb), homework @yadongli
11. ODE lecture slides (ipynb), homework @hcheng66
12. PDE lecture slides (ipynb), homework @hcheng66

Python setup

Python is the primary programming tool for this class. The following is a step by step instruction on how to set up the right Python environment for the class.

Python setup instructions

How to save/print slides as PDF

Similar steps should also work in other browsers, as long as you already have a PDF printer. The latest Chrome browser supports "Save as PDF" out of box.

Disclaimer

All course materials and codes are provided as-is, without any warranty for completeness or correctness.

Copyright notice

All python codes embedded in the class IPython notebook can be used free of charge by anyone. They are released under the open source MIT license, any derived work from the said Python codes shall reference to this github page.

The rest of the class materials, including but not limited to its contents, organization, lecture slides, derivations, examples, illustrations and homework problems etc, are copyrighted by the instructors. Permissions are granted for any educational institutions to use these class materials for educational purposes, conditioned on that this github page is referenced and attributed to. All other rights reserved.

Acknowledgement

The instructors want to thank our current and past students for pointing out many errors in the slides and homework.

Authors

@yadongli @hcheng66

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