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

Created by Hannes Schulz, Andreas Mueller and Nenard Birešev.

• The slides are at presentation/presentation_screen.pdf

• The interactive notebooks are in the main folder.

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
• 1 - PCA.ipynb
• 2 - KMeans.ipynb
• 3a - Linear regression 1D.ipynb
• 3b - Linear regression 2D.ipynb
• 4 - Logistic Regression.ipynb
• 5 - k Nearest Neighbors.ipynb

• 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.

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.

You can install everything at once using a complete scientific Python distribution. Two good ones are the Enthought Python distribution (EPD, free for academic use) or Python-(x, y) (free for everyone).

For OS X, you can also use the Enthought Python distribution or the scipy-superpack.

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

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

More tips on installing scikit-learn can be found on the scikit-learn website.

• The scikit-learn example gallery and user guide.
• Videos and presentations on machine learning and scikit-learn.
• The matplotlib example gallery.
• Numpy for matlab users.