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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