Supervised machine learning and performance evaluation

About this course

This course is designed for data scientists, machine learning practitioners, and graduate students who want to understand how to evaluate and select models reliably in real-world applications. It is particularly relevant for learners working with predictive models who need to ensure their results generalise beyond the training data. You’ll learn the statistical foundations behind performance estimation and gain hands-on experience with essential techniques such as cross-validation, model selection, and nested resampling. By the end of the course, you’ll be equipped to design robust evaluation workflows and make confident, evidence-based modeling decisions.

What you will learn

• Explain the assumptions required for reliable model performance estimation
• Understand how prediction performance estimates improve with increasing sample size
• Apply train-test splits and cross-validation to evaluate machine learning models
• Perform model selection and hyperparameter tuning using resampling methods