MAB608 Machine Learning

Overview Schedule and Reading Resources

Instructor: Daniel S. Menasche, Edmundo de Souza e Silva and Rosa M. Meri Leao
Time: Tuesdays and Thursdays 10am - 12
Place: H304A (Coppe) [later on, LEP1 - CCMN]
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Requisite: undergrad standing


In the past decade we experienced a dramatic growth in practical applications for machine learning. Yahoo! and Gmail use machine learning to filter spam. Google uses machine learning algorithms to analyze your historic data and predict likely future outcomes. At Ebay, machine learning is used to classify its products and to do contextual advertising. Given the pervasivity of machine learning in our lives, it is not surprising that they have gained the attention of many researchers.

The goal of this course is to provide an introduction to machine learning algorithms. Students will be exposed to machine learning tools such as linear regression, classifiers, neural networks, support vector machines, bayesian networks, hidden Markov models and Markov decision processes. The lectures will emphasize breadth over depth, and will balance practice and theory.

Course Requirements

The class will meet two times a week, for two hours, and will require student participation in several ways,

1) hand in homework assignments

2) solve programming exercises

3) prepare final project or present a paper