In the first version of our program, we fed our data into a cognitive agent fitted with a decision tree that is capable of predicting a winner for given a set of inputs. However, the instructor suggested us to use a more complex model such as neural nets mainly because they way human make decision is not as simple and straight forward as a decision tree.
Artificial Neural Networks model is originally inspired by biological neural networks. The idea is that this model is an interconnected group of nodes, similar to a vast network of neurons in a brain. Each node represents and a link between to nodes represent connection between those two neurons. This model has input nodes, hidden nodes which are responsible for the main part of the computation and output nodes.
The idea is to use neural nets to create a cognitive model which allow us to predict the results of soccer games between two teams. In other words, input to this model would be name of the two teams and the output of the model would be the prediction of the match results between those two teams. However, after reading about this model I think this is more like a black box which is hard to implement. In fact, I was not very clear how does the computation between hidden nodes happen.