How do Encoder/Decoder models learn in Deep Learning?
The encoder learns a compressed representation of the input data and the decoder tries to learn how to use just this compressed representation to reconstruct the original input data as best as possible. Let's say that the initial weights (usually randomly set) produce a reconstruction error of e. During training, both the encoder and decoder layer weights are adjusted so that e is reduced. 
Later on, usually, the decoder layer is removed and the output of the encoder layer (the compressed representation) is used as a feature map of the input.
What does compressed representation mean? If your input is an image of size 20 * 20 = 400 elements, your encoder layer might be of size 100 with a compression factor of 4. In other words, you are learning how to capture the essence of the data with 400 elements in only 100 while still being able to reconstruct the 400 element data with minimum error.
You are correct about filters being equivalent of nodes and changing the weights to learn the best representation for the input during training.
After learning a bit about encoder/decoder models in deep learning (mostly in Keras), i still cannot understand where the learning takes place.
Does the encoder just create the feature map and then the decoder tries to get as close as possible as the result with BackProp, or does the encoder learn as well when the model is trained?
One last question: if i understood correctly, the filters are the equivalent of the nodes in a classic machine learning model, changing its weights to learn, am i right?