【ML】Machine Learning Models

Neural Network: A neural network is a simplified model of the way the human brain processes information. There are typically three parts in a neural network: an input layer, with units representing the input fields; one or more hidden layers; and an output layer, with a unit or units representing the target fields.The units are connected with varying connection strengths(or weights). Input data are presented to the first layer, and values are propagated from each neuron to every neuron in the next layer. Eventually, a result is delivered from the output layer.

Bayesian Network:  A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph(DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. 

Probailistic Models: Probabilistic modeling is a statistical techinique used to take into account the impact of random events or actions in predicting the potential occurrence of future outcomes.

Naive bayes