  Non-asymptotic theory of random matrices strives to investigate the spectral
properties of random matrices, which are valid with high probability for
matrices of a large fixed size. Results obtained in this framework find their
applications in high-dimensional convexity, analysis of convergence of
algorithms, as well as in random matrix theory itself. In these notes we survey
some recent results in this area and describe the techniques aimed for
obtaining explicit probability bounds.
