$1.$ Introduction to Learning Processes:

How do various types of learning processes in artificial neural networks mimic human cognitive learning, and what are the primary differences in terms of adaptability and generalization?

$2.$ Error Correction Learning:

In what ways does error correction learning contribute to improving neural network performance? Discuss how backpropagation is an example of this mechanism.

$3.$ Memory$-$based Learning:

How does memory$-$based learning in ANNs differ from traditional parameter learning? Can you critically analyze the trade$-$offs between memory complexity and adaptability in such networks?

$4.$ Hebbian Learning: What is Hebbian learning, and how does it align with the biological principle of "cells that fire together, wire together"? How can this principle be applied to develop unsupervised learning algorithms?

$5.$ Competitive Learning:

How does competitive learning help in clustering and feature detection? Critically examine its role in unsupervised learning, particularly in Self$-$Organizing Maps $($SOMs$).$