Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and …
This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. …
Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of …
This course introduces first-year graduate students in the humanities and social sciences to the knowledge and skills fundamental to success in graduate school. Particular topics vary.
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, …
This course presents the simplest economic models explaining how individuals and organizations respond to changes in their circumstances and how they interact in markets, and it applies these models to …
The first part of a two-semester sequence in research methods and tools used to evaluate public policies. This course reviews basic mathematics and statistics used by policy analysts, and introduces …
Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced …
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 …
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, …
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 …
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 …
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 …
This course will provide a solid foundation of insights into how Congress works, essential for aspiring public policy advocates. Topics investigated include historical precedents for policymaking, the process of Congressional …
What are the most pressing policy problems facing Virginia and how can they be addressed? Students will learn how the broad historical forces of Virginia's past, her current political institutions, …
Students register for this course to complement an industry work experience. Topics focus on the application of engineering principles, analysis, methods and best practices in an industrial setting. A final …
Specialized or advanced topics not in DS current course offerings. Requires (a) approval of the program director and (b) an SDS faculty member who will serve as instructor. Propose a …
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. …
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. …
A colloquium on computational biology methods and results. Each week, students will attend a seminar, and read and discuss a computational biology paper, focusing on computational approaches and biological conclusions. …