Neuroinformatics, 2015-12-01

Module: 
Neuroinformatics
Examiner: 
Prof. Dr. Gordon Pipa
Assessor: 
Leon Sütfeld
Date: 
Tue, 2015-12-01

Neuroinformatics (mainly):
First I had to derive the Bayes Rule and to explain all its parts.
What is the Likelihood and why can we use it?
Then I had to describe the steps of the Maximum Likelihood Approach.
Explained the Fisher Information .
How to find out if model is good?
What can be problematic with your model ? Over – vs. underfitting..what does it mean?

For the Machine Learning part I’ve chosen “Concept Learning” :
Doesn’t seem to be a classic topic for the module exam. Maybe there are better ones to talk about but I liked it.
What is Concept Learning?
How can we handle with new data?

Generally speaking, the questions are very fair and, partly, open. So, you can explain a lot if you like and thus have enough opportunities to show what you’ve learned so far.
And now go for it ;-) good luck!

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