Very Large Information Systems Lecture & Lab

Course Description

This course introduces modern theories of machine learning and design & implementation models for large scale quantitative, image, and text information systems. The machine learning and information retrieval includes methodologies such as Boolean, vector space, probabilistic, inference net, and language modeling. Students will acquire hands-on experience by implementing models such as clustering algorithms, automatic text categorization, and experimental evaluation. As an introduction to data science theory and techniques, students will experiment with supervised and unsupervised learning algorithms, intelligent text summarization, topic detection, tagging, and tracking. The laboratory reinforces and expands learning of principles introduced in the lecture. Hands-on activities focus on implementing techniques for efficiently managing and manipulating very large data sets and build machine learning models. Prerequisites: MAT-374 and (CST-217 or SYM-400) and MAT-345.

Code

CST-425

Credits

4

Subject

Computer Science