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Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy structured/ relational data. Markov logic networks (MLNs), sets of weighted rstorder logic formulae, are a simple but powerful SRL formalism that generalizes both rst-order logic and Markov networks. MLNs have been successfully applied to a variety of real-world problems ranging from extraction knowledge from text to visual event recognition. Most of the existing learning algorithms for MLNs are in the generative setting: they try to learn a model that is equally capable of predicting the values of all variables given an arbitrary set of evidence; and they do not scale to problems with thousands of examples. However, many real-world problems in structured/relational data are discriminativewhere the variables are divided into two disjoint sets input and output, and the goal is to correctly predict the values of the output variables given evidence data about the input variables. In addition, these problems usually involve data that have thousands of examples. Thus, it is important to develop new discriminative learning methods for MLNs that are more accurate and more scalable, which are the topics addressed in this thesis. First, we present a new method that discriminatively learns both the structure and parameters for a special class of MLNs where all the clauses are non-recursive ones. Non-recursive clauses arise in many learning problems in Inductive Logic Programming. To further improve the predictive accuracy we propose a max-margin approach to learning weights for MLNs. Then to address the issue of scalability, we present CDA, an online max-margin weight learning algorithm for MLNs.