CSC 510 - Machine Learning and Data Science

Credit Hours: 3
Lecture Hours: 3
Lab Hours: 0

This course is designed to provide a combination of theoretical knowledge and practical, hands-on experience in solving real-world problems in data science through the application of machine learning and data mining. Topics include classification algorithms such as decision tree induction and support vector machines; cluster analysis with the k-means algorithm and hierarchical methods; outlier detection; mining of complex data types such as graphs and networks. This course also includes the recent and emerging area of deep learning, where it discusses architectures like convolutional and recurrent neural networks that have been designed to solve different classes of problems in computer vision, natural language processing and other areas. The course uses Python as the primary language, R as a secondary language, and introduces software tools that have been standardized for industrial applications of data science.
Prerequisite: STAT 540 (may be taken concurrently)

Academic Catalog

CSC 510 - Machine Learning and Data Science