A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments. A course covering statistical techniques such as regression.

Course provides an introduction to leadership in the public arena. Through course readings, team projects, and discussion of case studies, students will develop skill at identifying the resources, options, and constraints of leaders and followers in different organizational and political settings, writing policy memos, making professional policy presentations, developing negotiation strategies, managing uncertainty and stress, & working in teams.

Many problems in data science essentially boil down to some mathematical relationships that are to be solved numerically. But have you ever wondered how computers could do math? This graduate-level data science course aims to cover fundamental topics of scientific computing, specifically selected and curated for data scientists, including numerical errors, root finding algorithms, numerical linear algebra, and numerical optimization.

The purpose of this course is to develop the student's ability to define and solve public problems. Subsidiary objectives of the course are to help the student to integrate the analytical, political, and leadership skills they have learned in their other MPP courses and improve their ability to work in teams; and hone their written and oral presentation skills. Prerequisites: Graduate student in public policy

Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, Bootstrap methods, ROC curves, shrinkage, and some large-sample theory, prediction optimality versus estimation optimality.

In this course students will learn how to create change in the public policy arena by understanding political actors, their interests, and the institutions they inhabit. Students will learn how issues move through the policy process, at which points they are most amenable to influence, and how to create and use professional work products to influence them.

Introduces physics-aware deep learning (PADL), an emerging approach that embeds physical laws into neural networks for accurate, efficient modeling. Topics include differential equations, physics-informed neural networks, neural operators, and PyTorch implementation. Students gain both theoretical foundations and practical skills to apply PADL across disciplines.

Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and network centrality.

Combines topics in data ethics, critical data studies, public policy, governance, and regulation. Address challenges by topic (Health, Education, Culture & Entertainment, Security & Defense, Cities, Environment, Labor). Research how data-centric systems are deployed within socioeconomic ecosystems and shape the world. Interrogate connections between data science, governments, industry, civil society organizations, and communities.

Students in this course will contend with and explore the implications of how politically relevant attitudes & behaviors in the U.S. have always been tied to identity. Students will employ psychological insights on self, identity, and culture to examine the historical trajectories and broad identity-relevance of pressing social issues in the U.S. today.