Knowledge Discovery From Social and Information Networks
Descripción general del curso
Recent World Wide Web advances have resulted in large amounts of online data in many application domains such as Text Analysis, Social and Information Network Analysis, and Recommender Systems. Machine learning techniques offer promising approaches to the design of algorithms for training computer programs to effectively and efficiently analyze such data. Network analysis techniques help make sense of social and information networks accessible today in a highly inter-connected world.
The course will focus on understanding machine learning algorithms (including deep learning) 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, Naïve Bayes Classifiers, Logistic Regression, k-Nearest Neighbors, Artificial Neural Networks and Deep Learning models such as Convolutional Neural Networks and Recurrent Neural Networks. Examples of applications include: sentiment, emotions, and opinion mining; topic detection and classification; information extraction with focus on keyphrase extraction and relation extraction; recommender systems; mining Twitter for disaster response and recovery; image privacy prediction in online content sharing sites.
Each lecture will include a guided, hands-on exercise for students using publiclyavailable 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.
Condiciones de inscripción
Si un estudiante de la Escuela de Posgrado cursó ISIS-4519, solamente una de las dos materias será utilizada en el proceso de validación de requisitos de grado.
Información del curso
05 de Junio al 19 de Junio
Cornelia Caragea, Kansas State University
Lunes a viernes de 6:00 pm a 9:00pm, viernes de 5:00pm a 9:00 pm y sábado 8:30 am a 12:30 pm.
Válido como curso de profundización
Estudiantes otras maestrías:
Institución: Kansas State University
My research interests are in artificial intelligence, machine learning, data mining, information retrieval, and natural language processing, with applications to text and image analysis, and social network analysis.
The overarching goal of my research is to effectively and efficiently mine and discover knowledge from large amounts of data. I am particularly interested in information extraction, supervised and semi-supervised learning, privacy analysis, knowledge integration, and information and social network analysis.
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