CSC 381 - Seminar: Rotating Topics

Fall 2019

Linear Algebra in Data Mining
Instructor
Chartier

Data is a huge and growing part of our world. A major tool to manipulate and study this data is linear algebra. This course will introduce concepts of matrix algebra with an emphasis on application, particularly in the fields of computer graphics and data mining. We will learn such things as making an image transparent and compressing an image. In data mining, we will use linear algebra in such areas as cryptography, least squares approximation, compressed sensing, web search and sports ranking.

Prerequisites: CSC 121 and MAT 150
Counts as an elective toward the CSC and MAT majors and minors. 

Spring 2020

Recommender Systems
Instructor
Seminario

The ubiquitous “.. people who viewed this item also viewed these items .. ” recommendations found in online shopping applications are driven by underlying Recommender Systems. Started in the 1990’s, these systems have evolved from relatively simple news recommenders to today’s sophisticated recommender systems used by Amazon and Pandora. This course begins with an overview of the motivation for recommenders and the fundamental recommender methodologies: collaborative filtering, content-based, and hybrid. Then, we will do a deeper dive into the popular collaborative filtering (CF) algorithms: user-based, item-based, and matrix factorization. We will use Python programming and research datasets containing movie ratings to explore how these CF algorithms produce recommendations. Finally, we will learn how to evaluate recommender results with established research metrics and protocols as well as propose and evaluate custom modifications to recommender algorithms.

Satisfies Applications elective in the Computer Science major and minor.
Counts an an elective in the Data Science inerdisciplinary minor.

Prerequisites & Notes
Prerequisites:
CSC 221, MAT 150, and the ability to program in a high-level language such as Python, Java, or C++ at the level expected in CSC 221.