# tutorial_ml_gkbionics

**Repository Path**: mirrors_amueller/tutorial_ml_gkbionics

## Basic Information

- **Project Name**: tutorial_ml_gkbionics
- **Description**: A Tutorial on Simple Machine Learning Methods Held for the Graduate School on Bionics, 2012
- **Primary Language**: Unknown
- **License**: CC0-1.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-24
- **Last Updated**: 2026-04-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

tutorial_ml_gkbionics
=====================

A Tutorial on Simple Machine Learning Methods Held for the Graduate School on Bionics, 2012.

Created by Hannes Schulz, Andreas Mueller and Nenard Birešev.

Content
-------
- The slides are at [``presentation/presentation_screen.pdf``](https://github.com/amueller/tutorial_ml_gkbionics/raw/master/presentation/presentation_screen.pdf)

- The interactive notebooks are in the main folder.

Viewing the notebooks online
----------------------------
The content of the notebooks can be viewed online through nbviewer.ipython.org.
This is **not interactive**. You need to install Python on your computer to use the notebooks interactively.

- [0 - Python Intro.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/0%2520-%2520Python%2520Intro.ipynb)
- [1 - PCA.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/1%2520-%2520PCA.ipynb)
- [2 - KMeans.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/2%2520-%2520KMeans.ipynb)
- [3a - Linear regression 1D.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/3a%2520-%2520Linear%2520regression%25201D.ipynb)
- [3b - Linear regression 2D.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/3b%2520-%2520Linear%2520regression%25202D.ipynb)
- [4 - Logistic Regression.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/4%2520-%2520Logistic%2520Regression.ipynb)
- [5 - k Nearest Neighbors.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/temporaer/tutorial_ml_gkbionics/master/5%2520-%2520k%2520Nearest%2520Neighbors.ipynb)

More content
------------
- The latex source code for the slides is contained in ``presentation``.
- The notebooks used to generating some of the figures on the slides is in ``working_notebooks``.


Installing Python
-----------------
For a true interactive use of the notebooks you need to install Python, IPython (for notebooks) and the required libraries scikit-learn, matplotlib and numpy.

Windows
-------
You can install everything at once using a complete scientific Python distribution.
Two good ones are the [Enthought Python distribution](http://www.enthought.com/products/epd.php) (EPD, free for academic use) or  [Python-(x, y)](http://code.google.com/p/pythonxy/) (free for everyone).

Mac
---
For OS X, you can also use the [Enthought Python distribution](http://www.enthought.com/products/epd.php) or the [scipy-superpack](http://fonnesbeck.github.com/ScipySuperpack/).


Linux
-----
Just use your package manager, for example on ubuntu or debian, use
``apt-get install python ipython python-matplotlib python-numpy python-sklearn``.

Version requirements
--------------------
You need to make sure to have at least IPython >= 0.11 installed. You can update using the programm ``easy_install``.

Installing Scikit-learn
-----------------------
More tips on installing scikit-learn can be found on the [scikit-learn website](http://scikit-learn.sourceforge.net/dev/install.html#installing-an-official-release).


More Resources
--------------
- The [scikit-learn example gallery](http://scikit-learn.sourceforge.net/dev/auto_examples/index.html) and [user guide](http://scikit-learn.sourceforge.net/dev/user_guide.html).
- [Videos and presentations](http://scikit-learn.sourceforge.net/dev/presentations.html) on machine learning and scikit-learn.
- The [matplotlib example gallery](http://matplotlib.org/gallery.html).
- [Numpy for matlab users](http://www.scipy.org/NumPy_for_Matlab_Users).