the basic idea of maximum likelihood is that we write down a density function that the model describes that I represent with P model of X X is a vector describing the input and P model of X is a distribution controlled by parameters theta that describes exactly where the data concentrates and where it is spread more thinly maximum likelihood consists in measuring the log probability that this density function assigns to all the training data points and adjusting the parameters theta to increase that probability the way that