Requirements
- The course includes an introduction to the decision trees algorithm so the only requirement for the course is a basic knowledge of spreadsheets and R. I hope you are ready to upgrade yourself and learn to optimize investment portfolios with excel and R.
Description
Would you like to build predictive models using machine learning? That´s
precisely what you will learn in this course “Decision Trees, Random
Forests and Gradient Boosting in R.” My name is Carlos Martínez, I have a
Ph.D. in Management from the University of St. Gallen in Switzerland. I have
presented my research at some of the most prestigious academic conferences and
doctoral colloquiums at the University of Tel Aviv, Politecnico di Milano,
University of Halmstad, and MIT. Furthermore, I have co-authored more than 25
teaching cases, some of them included in the case bases of Harvard and
Michigan.
This is a very comprehensive course that includes presentations,
tutorials, and assignments. The course has a practical approach based on
the learning-by-doing method in which you will learn decision trees and
ensemble methods based on decision trees using a real dataset. In addition to
the videos, you will have access to all the Excel files and R codes that
we will develop in the videos and to the solutions of the assignments included
in the course with which you will self-evaluate and gain confidence in
your new skills.
After a brief theoretical introduction, we will illustrate step by
step the algorithm behind the recursive partitioning decision trees. After
we know this algorithm in-depth, we will have earned the right to automate it
in R, using the ctree and rpart functions to respectively construct
conditional inference and recursive partitioning decision trees. Furthermore,
we will learn to estimate the complexity parameter and to prune trees to increase
the accuracy and reduce the overfitting of our predictive models. After
building the decision trees in R, we will also learn two ensemble methods based
on decision trees, such as Random Forests and Gradient Boosting. Finally, we
will construct the ROC curve and calculate the area under such curve, which
will serve as a metric to compare the goodness of our models.
The ideal students of this course are university students and
professionals interested in machine learning and business intelligence. The
course includes an introduction to the decision trees algorithm so the only
requirement for the course is a basic knowledge of spreadsheets and R.
I hope you are ready to upgrade yourself and learn to optimize
investment portfolios with excel and R. I´ll see you in class!
Who this course is
for:
- The ideal students of this course are university students and professionals interested in machine learning and business intelligence.
