Covers the fundamental concepts of uncertainty in artificial intelligence (AI). Students will explore various techniques and models used to handle uncertainty in AI and machine learning systems, including Bayesian deep learning, dropout as a Bayesian approximation, and decision theory. Will also cover applications of uncertainty in AI, such as computer vision, natural language processing,and autonomous systems.

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

Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, sampling distributions, discrete and continuous time Markov chains, and Brownian motion.

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.

Project oriented course that will research specific climate problems, proposing new solution to decision makers at local & state level. Course expands understanding of broad societal scope relevant to climate action. Students are exposed to federal, state & local policy challenges and opportunities as well as understanding how business & politics shape the policy landscape. Gain understanding of diverse climate-relevant career opportunities.

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.

Course uses expert entrepreneurs with decades of starting & running new ventures. Expert entrepreneurs learn to tackle the unpredictable, but also to embrace and leverage it to cocreate enduring new ventures. Students will grapple with the principles and process of effectual action and interaction. The course is designed to delve into effectual entrepreneurship ¿ philosophically, psychologically and practically.

Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and random forests. Students will be required to fit the models (both MLE and Bayesian) and use the strategies discussed in class.

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.

Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with pandas, data acquisition via flat files, APIs, JSON formats, and webscraping, relational, document, and graph databases, exploratory data analysis including static & interactive data visualization, dashboards, and cloud computing.