90-904 Machine Learning in Policy
12 units, Carnegie Mellon University
Machine learning, a field derived primarily from computer science and statistics, has matured and gained wide adoption over past decades. Alongside exponential increases in data measurement and availability, the ability to develop appropriate and tailored analyses is in demand. As practitioners in the social sciences consider machine learning methods, however, we are identifying limitations and externalities of the applications of machine learning techniques, such as overconfidence in settings with concept drift, lack of generalizability due to selection bias, and magnification of inequities. Machine Learning and Policy seeks to 1 demonstrate motivations and successes of machine learning, to 2 contrast them with more classical methods, and to 3 investigate the promise and cautions of machine learning for public policy. The course will cover variety of topics, including Basics of machine learning probabilityBayeslikelihoodconjugacy, terminology, codealgorithm design, evaluation, mathematical formulations Popular and wellperforming methods random foreststreesensembles, neural networksbackpropagationembeddingsgeneralized adversarial networks, generalized linear modelsshrinkageconvexitybasis functions, support vector machineskernelsoptimizationLagrangian Leveraging other data sources natural language processingtopic modelingrelational noni.i.d.relational Markov logic networkstemporal data Additional topics causalityconfoundingpropensity scoringinverse weightingcausal directed acyclic graphs, fairnessethics, interpretationexplanationvisualization, anomaly detection, semisupervised and active learning, reinforcement learning a hrefhttpwww.heinz.cmu.eduacademicresourcescourseresultscoursedetailsindex.aspxcid163...Read Morea
Prerequisites: none listed
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