Data Analytics and Computation 695M - S-Machine Learning/Social Sci

This course will provide an overview of machine learning (ML) with special attention to applications for social and behavioral analytics. Machine learning combines insights from artificial intelligence, probability theory, statistical inference, and information theory to help automate tasks involving pattern recognition, prediction, and classification. "Learning" is analogous to "inference" in statistics and, in fact, the modern statistical toolkit includes various machine learning methods developed to handle large (and messy) datasets. The course focuses on statistical learning and is a good second or third course in statistical methods for graduate students in the social and behavioral sciences. We will examine key techniques of supervised and unsupervised learning and reflect upon appropriate and inappropriate applications of such approaches for those seeking to understand the social world. We shall also discuss the ethical issues involved in automated analysis and computer-assisted decision-making, including how they may in some cases help overcome human biases and in others instead only serve to reinforce these tendencies.

Open to DACSS students. This course fills a technical elective requirement for the DACSS Masters degree. It is open to DACSS masters students only.

This course assumes a working knowledge of R. If you do not have a strong background in R (e.g., have not already taken and passed DACSS 601 Data Science Fundamentals or an equivalent), please contact the instructor to discuss strategies for preparing for this course.

There will also be an additional optional class meeting on Wednesdays at 7:00 pm.