MINE – 4205

Nombre del curso: Knowledge Discovery From Social and Information Networks
Course Name: Knowledge Discovery From Social and Information Networks
Créditos: 4
Profesora: Cornelia Caragea – Kansas State University


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.

El curso es válido como Profundización para estudiantes de MINE, electivo para otras maestrías de la Escuela de Posgrado del Departamento.

El curso de ofrece en inglés.


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.


Basic knowledge on probability and statistics, data structures, programming, and algorithms.

Background in machine learning or social and information network analysis is not required.

El curso se ofrece entre el 5 y el 19 de junio de 2018, en el siguiente horario:

5, 6, 7, 8 de junio: 18h00 a 20h50

9 de junio: 09h00 a 12h50

12, 13 y 14 de junio: 18h00 a 20h50

15 de junio: 17h00 a 20h50

16 de junio: 09h00 a 12h50

18, 19 de junio: 18h00 a 20h50