Data Analytics and Computation 756 - MachineLearningSocialScientist

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 masters students and SBS PhD students only Fulfills a technical elective requirement for MS DACSS program. Recommended preparation for this course: DACSS 601 and DACSS 603 or equivalent. This course assumes a working knowledge of R and previous coursework in statistical methods, including linear regression. If you do not have a strong background in R or Python (e.g., have not already taken and passed DACSS 601 Data Science Fundamentals or DACSS 690P Intro to Python for Data Science), please contact the instructor before enrolling. Please contact the instructor (or DACSS@UMass.edu if no instructor assigned) if you would like to enroll in this class but are not in one of the eligible groups.