Neuroinformatics, 2015-12-01

Module: 
Neuroinformatics
Examiner: 
Prof. Dr. G Pipa
Assessor: 
O. Stojanovic
Date: 
Tue, 2015-12-01

15-20 min Neuroinformatics

-What is Bayes rule? How to derive it.
-In the lecture we use likelyhood, why?
-How can a model look like and how do we get it? (linear regression-normal distribution)
-What makes this model linear, where is it linear?
-Explain LL, list the steps?
-what is the 1./2. derivative. He drew two functions-draw the derivative
-Is the model overall normal distributed. (The noise is overall nd)
-Model evaluation

10-15 min Computer Vision

- Feature extraction, what can u tell me about it.
(Started with template matching, then SIFT, Viola-Jones, SURF)
- What are Haar wavelets, what do they do?
- How can I detect edges

Grade 1.7