The material for the Quantitative Big Imaging Course at ETHZ
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README.md first version of deep learning exercises May 26, 2016

README.md

Quantitative Big Imaging Course 2016

Here are the lectures, exercises, and additional course materials corresponding to the spring semester 2016 course at ETH Zurich, 227-0966-00L: Quantitative Big Imaging.

The lectures have been prepared and given by Kevin Mader and guest lecturer Anders Kaestner. Please note the Lecture Slides and PDF do not contain source code, this is only available in the handout file. Some of the lectures will be recorded and placed on YouTube on the QBI Playlist.

Lectures

Exercises

General Information

The exercises are based on the lectures and take place in the same room after the lecture completes. The exercises are designed to offer a tiered level of understanding based on the background of the student. We will (for most lectures) take advantage of an open-source tool called KNIME (www.knime.org), with example workflows here (https://www.knime.org/example-workflows). The basic exercises will require adding blocks in a workflow and adjusting parameters, while more advanced students will be able to write their own snippets, blocks or plugins to accomplish more complex tasks easily. The exercises from last year (available on: kmader.github.io/Quantitative-Big-Imaging-2015/) are done entirely in ImageJ and Matlab for students who would prefer to stay in those environments (not recommended)

Assistance

The exercises will be supported by Yannis Vogiatzis, Kevin Mader, and Christian Dietz. There will be office hours in ETZ H75 on Thursdays between 14-15 or by appointment.

Specific Assignments

Feedback (as much as possible)

  • Create an issue (on the group site that everyone can see and respond to, requires a Github account), issues from last year
  • Provide anonymous feedback on the course here
  • Or send direct email (slightly less anonymous feedback) to Kevin

Final Examination

The final examination (as originally stated in the course material) will be a 30 minute oral exam covering the material of the course and its applications to real systems. For students who present a project, they will have the option to use their project for some of the real systems related questions (provided they have sent their slides to Kevin after the presentation and bring a printed out copy to the exam including several image slices if not already in the slides). The exam will cover all the lecture material from Image Enhancement to Scaling Up (the guest lecture will not be covered). Several example questions (not exhaustive) have been collected which might be helpful for preparation.

Projects

  • Overview of possible projects
  • Here you signup for your project with team members and a short title and description

Other Material