ImageXD 2016
============
This course material is available at
https://github.com/scikit-image/skimage-tutorials
Welcome to BIDS!
----------------
Welcome to the `Berkeley Institute of Data Science
<https://bids.berkeley.edu>`__ on the campus of the University of
California at Berkeley.
- Restrooms are out the back door of BIDS, down the corridor to the
right (i.e., at the southern entrance of the Doe Library). **You
cannot re-enter BIDS through the back door--please take the
(inside) corridor to the front entrance**.
- Wifi is available via the CalVisitor hotspot.
Conversation
````````````
During the tutorial, there will be an open Etherpad at
https://public.etherpad-mozilla.org/p/imagexd where you can make notes
and communicate with classmates.
Schedule
--------
- 09:30—10:00 :doc:`Introduction <lessons/0_introduction>` & :doc:`preparation <lessons/0_preparation>`
- 10:00—10:45 :doc:`Advanced NumPy <lessons/1_advanced_numpy>`
- 10:45—11:00 Coffee break
- 11:00—11:30 :doc:`Advanced NumPy <lessons/1_advanced_numpy>`
- 11:30—12:15 `Matplotlib <http://cbio.ensmp.fr/~nvaroquaux/teaching/2016-image-xd/intro/matplotlib/matplotlib.html>`_
----
- 12:15—13:15 Lunch
----
- 13:15—14:00 `SciPy overview <http://cbio.ensmp.fr/~nvaroquaux/teaching/2016-image-xd/intro/scipy.html>`_
- 14:00—14:30 :doc:`scikit-image: images are arrays <lessons/0_images_are_arrays>`
- 14:30—14:45 Coffee break
- 14:45—15:45 scikit-image: `filtering
<http://nbviewer.jupyter.org/github/scikit-image/skimage-tutorials/blob/master/book/lessons/1_image_filters.ipynb>`__
and `segmentation
<http://nbviewer.jupyter.org/github/scikit-image/skimage-tutorials/blob/master/book/lessons/4_segmentation.ipynb>`__
- 15:45—16:15 :doc:`scikit-image: RANSAC <lessons/1_ransac>`
- 16:15—16:25 Coffee break
- 16:25—16:30 Panorama demo
- 16:30—17:00 `Introduction to scikit-learn <http://cbio.ensmp.fr/~nvaroquaux/teaching/2016-image-xd/intro/sklearn.html>`_
- 17:00—17:30 Demo: scikit-learn with scikit-image
.. toctree::
:hidden:
:maxdepth: 1
lessons/0_introduction
lessons/0_preparation
lessons/1_advanced_numpy
Matplotlib <lessons/matplotlib/matplotlib>
lessons/0_images_are_arrays
lessons/1_ransac
