The goal of this seminar is to examine in detail proeminent Machine Learning approaches. Machine Learning (also known as Data Mining, Pattern Recognition, Data Analysis, and Classification) is a research area at the intersection of computer science, artificial intelligence, mathematics and statistics, that addresses the problem of developing software systems able to improve their effectiveness with experience. It can be applied to a vast set of applications such as predicting customer behavior, steering a robot, detect spam, and predict the folding of a protein, to name just a few. In this seminar, state-of-the-art approaches to Machine Learning will be analyzed as well as their application to real world problems.

Instructor: Lucas Rêgo Drumond