Program Overview

Introduction to Statistical Machine Learning

The course gives an overview of some of the most well-known statistical machine-learning concepts, techniques, and algorithms. It is aimed at students and researchers who already have some knowledge of statistics and probability theory and have used the statistical software R/RStudio.

Course Details

• The course will be held over four consecutive Wednesdays, and attendance is required for all sessions.
• Topics Covered:
  • An Overview of Classification & Prediction
  • Misunderstandings about Prediction versus Classification
  • Binary Logistic Regression Model
  • Describing the Fitted Statistical Model
  • Indexes of Model Performance
  • The Bootstrap
  • Model Validation
  • Quantities to be validated
  • Data-Splitting
  • Leave-One-Out Cross-Validation
  • k-fold cross-validation
  • Validation Using the Bootstrap
  • Bootstrapping Ranks of Predictors
  • Exercises

Instructor

Amar Ahmad

"Research Scientist / Biostatistician"

Course Contents and Structure

The course is designed to provide a comprehensive introduction to statistical machine learning, covering both theoretical foundations and practical applications using R/RStudio.

Note

Registration for the course is now closed.