ISIS 4519 Knowled Discov in Soc Networks

The first part of the course will focus on understanding machine learning algorithms and identifying challenging problems on the Web, learning how to apply machine learning algorithms to these problems, and how to use the existing tools and design new ones. Examples of topics include: supervised learning techniques, e.g., text classification, kernel methods and Support Vector Machines, Bayesian learning, Artificial Neural Networks and Deep Learning techniques, as well as semi-supervised learning techniques. The second part of the course will focus on modeling social networks. Examples of topics include: what are networks and why do we study them; describing and measuring networks (e.g., centrality, degrees, diameters); community (e.g., clustering, community structure); opinion mining, coordination and cooperation. Each lecture will include a guided, hands-on exercise for students using publicly-available machine learning and data mining tools on large document collections obtained from well-known digital library portals and social media sites. Prerequisites: Basic knowledge on probability and statistics, data structures, programming, and algorithms. Background in machine learning or social and information network analysis is not required.



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