INFSCI 2165 ADVANCED TOPICS IN DATA MINING: SOCIAL & HETEROGENEOUS GRAPH MINING

This course will cover a number of advanced topics in data mining, with special attention to graph/network mining, recommender systems, and techniques for analyzing large-scale, heterogeneous, high-dimensional, and multi-relational data in various domains -- e.g., web science, social science, and health and natural sciences. A mix of lectures, readings, and homework assignments will be offered to familiarize the students with recent methods and algorithms in these topics, as well as the basic concepts and theoretical foundations of these advanced techniques. The course is for students with prior background in data mining or machine learning techniques. It is a good option for graduate students who are interested in gaining in-depth knowledge or research experience in the field, or students from other disciplines who need to develop data mining systems to analyze large amounts of heterogeneous data. Hands-on experience in data analytics and machine learning with large datasets is required.

Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis
Course Requirements: PREQ: INFSCI 2595 and PLAN = ISCl-MSl, ISCl-AC, BDAL-ACG, SAISYS-ACG, SAIS-ACG, ISCl-PHD,INFSCl-MSI, INFSCl-AC, INFSCl-PHD
Minimum Credits: 3
Maximum Credits: 3