In the present paper, we propose the model of {it structural information learning machines} (SiLeM for short), leading to a mathematical definition of learning by merging the theories of computation and information. Our model shows that the essence of learning is {it to gain information}, that to gain information is {it to eliminate uncertainty} embedded in a data space, and that to eliminate uncertainty of a data space can be reduced to an optimization problem, that is, an {it information optimization problem}, which can be realized by a general {it encoding tree method}. The principle and criterion of the structural information learning machines are maximization of {it decoding information} from the data points observed together with the relationships among the data points, and semantical {it interpretation} of syntactical {it essential structure}, respectively. A SiLeM machine learns the laws or rules of nature. It observes the data points of real world, builds the {it connections} among the observed data and constructs a {it data space}, for which the principle is to choose the way of connections of data points so that the {it decoding information} of the data space is maximized, finds the {it encoding tree} of the data space that minimizes the dynamical uncertainty of the data space, in which the encoding tree is hence referred to as a {it decoder}, due to the fact that it has already eliminated the maximum amount of uncertainty embedded in the data space, interprets the {it semantics} of the decoder, an encoding tree, to form a {it knowledge tree}, extracts the {it remarkable common features} for both semantical and syntactical features of the modules decoded by a decoder to construct {it trees of abstractions}, providing the foundations for {it intuitive reasoning} in the learning when new data are observed.
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