This course is an introduction to statistical learning using R. In this course, students are exposed to a collection of relatively simple statistical models with varying degrees of complexity. The emphasis is on the hands-on application of machine learning methods on various datasets rather than a theoretical treatment. This is a standard 1 semester course with a lab component. Previous experience with a statistical programming language is recommended but not required. Likewise, previous knowledge in college-level calculus, linear algegra, and statistics is recommended but not required. The core structure of the course is as follows:

1. An Introduction to R
   • Data Types
   • Data Structures
   • Functions, Packages
   • Control Structures, Debugging
   • Plotting
2. Regression
   • k Nearest Neighours Regression
   • Regression Trees
   • Gradient Descent
   • Linear Regression
3. Classification
   • k Nearest Neighours Classification
   • Classification Trees
   • Logistic Regression
   • Discriminant Analysis
   • Support Vector Machines
   • Neural Networks
4. Model Evaluation and Selection
   • Evaluation, Confusion Matrix, and the ROC curve
   • Cross-validation
   • Feature Selection