  These notes review six lectures given by Prof. Andrea Montanari on the topic
of statistical estimation for linear models. The first two lectures cover the
principles of signal recovery from linear measurements in terms of minimax
risk. Subsequent lectures demonstrate the application of these principles to
several practical problems in science and engineering. Specifically, these
topics include denoising of error-laden signals, recovery of compressively
sensed signals, reconstruction of low-rank matrices, and also the discovery of
hidden cliques within large networks.
