Difference between revisions of "Main Page"

From Protein Prediction 2 Winter Semester 2014
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This is the main information exchange for the exercise 'Protein Prediction I' for Computer Scientists. The organisation, requirements and grading of the exercise is explained here by the mentors. All students will have one log in account and may add content to this wiki, if required by the mentors.
this is also a test
 
   
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= Exercise 'Protein Prediction I' for Computer Scientists =
this is also a test 2
 
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== Grading ==
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The exercise grade accounts for 60% of the final lecture grade. The grading will be based on the visualization component you will develop during the exercise, its clear and complete documentation, and the final presentation of the component to the group including Prof. Rost.
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== Mentors ==
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The mentors for the exercises will be:
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* [https://rostlab.org/~goldberg/ Tatyana Goldberg] - a PhD student at RostLab, who is a newbie to JavaScript and a former org admin and mentor of a JavaScript project in the [http://www.google-melange.com/gsoc/homepage/google/gsoc2014 Google Summer of Code Program]
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* [http://www.linkedin.com/pub/juan-miguel-cejuela/31/25b/597 Juan Miquel Cejuela] - a PhD student at RostLab
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Our exercises will heavily benefit from the expertise and enthusiasm of:
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* Sebastian Wilzbach
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* David Dao

Revision as of 16:11, 10 October 2014

This is the main information exchange for the exercise 'Protein Prediction I' for Computer Scientists. The organisation, requirements and grading of the exercise is explained here by the mentors. All students will have one log in account and may add content to this wiki, if required by the mentors.

Exercise 'Protein Prediction I' for Computer Scientists

Grading

The exercise grade accounts for 60% of the final lecture grade. The grading will be based on the visualization component you will develop during the exercise, its clear and complete documentation, and the final presentation of the component to the group including Prof. Rost.

Mentors

The mentors for the exercises will be:

Our exercises will heavily benefit from the expertise and enthusiasm of:

  • Sebastian Wilzbach
  • David Dao