This course focuses on foundations, techniques, applications and algorithms for conducting predictive analytics on problems that involve significant text data, including webpages, social media, ‘natural language’ documents and even graphs. Students will learn the practical aspects of the techniques needed to build predictive analytical systems over text data. Today, many of these systems are applications of machine learning, including supervised and unsupervised learning. Topics include information retrieval (including search and indexing), natural language processing (including information extraction and entity linking), and knowledge discovery. The class will be run as a fast-paced lecture course with lots of student participation and significant hands-on experience. As an integral part of the course each student will do a project using the research and tools covered in the class. The class will occasionally feature guest lecturers with advanced knowledge in some of the covered topical areas.
The course is offered as a graduate-level elective in the Department of Industrial and Systems Engineering (ISE), usually every fall. Here's a typical copy of the syllabus.