Objetivos

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.