Markov vs Feedforward

$\frac{1}{3}$ points $($graded$)$

What are some advantages of the feedforward NN as described in the lecture versus Markov models? $($Choose

all that apply.$)$

They contain a fewer number of parameters

We can easily control the complexity of feedforward NN by introducing hidden layers

They are able to encode more complex transition probabilities than Markov Models.