Machine Learning for Business Using R

Introduction

In finance, marketing, consulting, public policy, science, you name it, people who can make sense of large and complex data sets are in high demand. Machine learning refers to a vast set of tools that helps us in making sense of data. This course gives an introduction to various machine learning methods, concentrating on those that fall under the umbrella of supervised learning. The course contains both model based and algorithm based machine learning approaches, contrasting the advantages and limitations of the two. The course contains some mathematical statistics meant to enable the students to peek into various algorithms. We will use the programming language R.

Course content

• Introduction to R.
• What is machine learning.
• Linear regression, including dimension reduction methods.
• Classification. Logistic regression, linear discriminant analysis, naive Bayes, and K nearest neighbours.
• Bootstrapping and other resampling methods.
• Tree-based methods. Bagging, random forests, boosting.
• Data cleaning.