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Question: here is a problem about hebb learning and associative memory...

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Here is a problem about Hebb learning and associative memory.

For the first question, it doesn't need to answer the concept (what is PCA), but I want to know what do the following questions mean?

I want to get solution as detailed as possible. I will appreciate it so much!

Question 3 (8+7-15 marks) Answer the following questions with regard to Hebb learning and associative memory models 1. What is Principal Component Analysis (PCA)? Which eigen value indicates the direction of largest variance? In what sense is the representation obtained from a projection onto the eigen directions corresponding to the largest eigen values optimal for data reconstruction? [8 marks] 2. Determine the weight matrix for an auto-associative, and discrete Hopfield Network that has four neurons and has learned the patterns: P 1 -1 -,P21-1 7 marks]

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