## Description

## For each of the below statements, agree or disagree and explain your

reasoning:

Hierarchical clustering can’t handle big data well but K Means

clustering can. This is because the time complexity of K Means is

linear i.e. O(n) while that of hierarchical clustering is quadratic i.e.

O(n ).

In K Means clustering, since we start with random choice of clusters,

the results produced by running the algorithm multiple times might

differ. The same is true in Hierarchical clustering since the order of the

data can be random.

K Means clustering requires prior knowledge of K i.e. no. of clusters

you want to divide your data into. But, you can stop at whatever

number of clusters you find appropriate in hierarchical clustering by

interpreting the dendrogram.

For feed forward neural networks, explain what can be represented.

Explain how weights are updated in the back propagation step.

When are CNN useful? In a CNN, how are the number of connections

reduced in comparison to a feed forward NN? Explain pooling layer and

convolution layer.

What is the difference between bias and variance? How do these relate

to underfitting and overfitting? How can they help you select models?

How are they used to tune hyperparameters?

When are RNN useful? What is the shortcoming of a simple RNN and

how is that solved using LSTM?