CSPN Alternative Spring Break

The University of Maryland Data Science Center coordinates graduate-level research and education across machine learning, artificial intelligence, and statistical analysis domains. The Fall 2023 seminar series features presentations on causal inference, adversarial learning, and privacy-preserving methods from researchers at partner institutions. Core programming includes deep neural network interpretation and high-dimensional statistics workshops delivered through structured learning modules. Dr. Robert P. Goldman leads the DS-700 Applied Machine Learning course, focusing on supervised learning, unsupervised algorithms, and implementation frameworks. Students work directly with Python-based tools including scikit-learn, TensorFlow, and PyTorch throughout the semester. The curriculum emphasizes hands-on development of machine learning applications using current industry standard libraries and practices. The Economics Department provides structured guidance for doctoral candidates completing dissertations in quantitative fields. Documentation covers committee selection protocols, prospectus development requirements, and defense scheduling procedures. The materials detail specific milestones from initial proposal through final manuscript submission, integrating with broader Data Science Center resources.