In this lecture, we will go beyond Friday's course on structured sparsity, and consider more complex models. Recently, a large amount of research in statistics and signal processing has been devoted to developing structured sparse regularization functions. The goal is to encode some a priori knowledge about an estimation problem in the regularization, in order to obtain better prediction or better interpretability. Unfortunately, the literature on the topic is is vast, and significantly different approaches are now refered to as ``structured sparsity''. We will present some aspects of this literature, and focus particularly on convex approaches for structured sparse estimation. References: [1] F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Structured Sparsity through Convex Optimization. Statistical Science. 27(4). 2012 http://projecteuclid.org/euclid.ss/1356098550