Knowledge Discovery From Social and Information Networks 

Descripción general del curso

Course Objective:

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

Cupos:

Válido como:

Profesor(es)

Evento patrocinado por :

Comparte esto en: