COSC 526 - Data Mining

3 Credit Hours

Will focus on understanding the statistical structure of large-scale (big) datasets using machine learning (ML) algorithms. We will cover the basics of ML and study their scalable versions for implementation within distributed computing frameworks. We will pursue ML techniques such as matrix factorization, convex optimization, dimensionality reduction, clustering, classification, graph analytics and deep learning, among others. We will emphasize algorithmic development for big data mining in three different, but general scenarios: (1) when available memory is extremely large; (2) when available memory is small, but can be distributed across a cluster (e.g., cloud-like environments); and (3) when the available memory is small and data has to be analyzed “in-situ” or “online” (e.g., streaming environments). The course will be project driven with source material from a variety of real-world applications. Students will be expected to design, implement and test their ML solutions.
Recommended Background: Machine Learning.

Undergraduates

Graduate Students

COSC 526 - Data Mining