Seminars of Innovation and Research in Informatics

This course aims to provide an extremely practical vision of the application to real problems of modeling techniques, from the most basic to the most state-of-the art ones, in highly quantitative business environments (banking and finance). The goals of this course is to learn the challenges involved in creating a model in the real world from obtaining the data to the best model to be used and the validation mechanisms depending on the type of problem. Good practices and description of the usual problems in a large modeling project.

Knowing how to classify a categorical response variable based on information provided by predictor variables is a necessary skill for any data scientist. This course will teach statistical and machine learning methods for classification and illustrate their application by analyzing a variety of real datasets using R. The participants will be given a similar dataset to analyze to gain hands-on experience.Course goalsThis course will provide a hands-on training in statistical and machine learning methods for classification, their implementation using R, and their application.Course contents The course will be taught using the book, An Introduction to Statistical Learning with Applications in R, by James, Witten, Hastie, and Tibshirani, Springer, 2013. A free PDF copy of the book can be downloaded from http://www-bcf.usc.edu/~gareth/ISL/. The following is a tentative schedule and list of topics to be covered: