# Octave

**Repository Path**: dailaimayun/Octave

## Basic Information

- **Project Name**: Octave
- **Description**: my octave exercises for 2011 stanford machine learning class, posted after the due date of course
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-11-26
- **Last Updated**: 2024-05-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

> I took this class 5+ years ago. Please don't open any issues or pull requests. All examples come as is.

# Octave

[Octave](http://www.gnu.org/software/octave/) is a high level (open source) programming language similar to Matlab. I'm using it for the [2011 Stanford Machine Learning Class](http://www.ml-class.org).

## Do Androids Sing Electric Operas?

This repo contains my Octave homework assigned that I have completed. If you are taking the class please fork the project and put different solutions in a different branch. However please do not post
any code before the due date (homework is due every sunday). I will not post solutions before the due date, so please don't ask.


## Other Notable Open Source Machine Learning Sources

https://github.com/everpeace/ml-class-assignments

https://github.com/joewandy/Stanford-Machine-Learning



## Development

To get Textmate Highlighting check out the [Octave Bundle](https://github.com/textmate/matlab.tmbundle) Just download and change the extension to `.tmbundle` and double click. After that go to Bundles >> Bundle Editor >> Reload Bundles.

Select 'Matlab' as the file type when working with octave files



## Stanford Honor Code

"We strongly encourage students to form study groups, and discuss the lecture videos (including in-video questions). We also encourage you to get together with friends to watch the videos together as a group. However, the answers that you submit for the review questions should be your own work. For the programming exercises, you are welcome to discuss them with other students, discuss specific algorithms, properties of algorithms, etc.; we ask only that you not look at any source code written by a different student, nor show your solution code to other students."





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## License

Solutions licensed under MIT License
Copyright (c) 2011 [Schneems](http://twitter.com/schneems). See LICENSE.txt for
further details.