CSCI 397 - Seminar

Credits: 3
Planned Offering: Offered when interest is expressed and departmental resources permit.

Prerequisite: Instructor consent. Readings and conferences for a student or students on topics agreed upon with the directing staff. May be repeated for degree credit if the topics are different. A maximum of six credits may be used toward the major requirements.

Fall 2014 topics:

CSCI 397-01: Seminar: Techniques in Big Data (3). Prerequisite: Instructor consent. The term “Big Data” encompasses the acquisition, analysis, and visualization of high-dimension, high sample-size data to inform decision making – problems with many dimensions and many samples make for “Big” problems. However, “Big” also refers to the central role of Big Data analysis in our daily lives, where it affects outcomes in medicine, security, advertising and recommendation systems, transportation, and many other domains. In this course, we learn and apply some of the field’s core algorithms and techniques, with an emphasis on three application domains agreed upon at the beginning of the term. Specific technical topics may include feature selection, linear and Bayesian discriminants, principal component analysis and dimensionality reduction, clustering, neural networks, and random forest learning. Salan.