OT-SC-WS-03 | Machine learning

Contents

This workshop teaches the major concepts of machine learning. We focus on general principles instead of going into full depth of single methods. We will learn the difference between supervised and unsupervised learning, important notation such as models and learners, why training errors are different than test errors and how to optimize and evaluate prediction performance. Nevertheless, the course covers the most important machine learning methods such as k-nearest neighbors, decision trees, random forests, boosting, support vector machines and artificial neural networks. The methods will be introduced in a non-technical and intuitive way. These theory sessions will be complemented by hands-on sessions in R, where the methods are applied in practice.

Outcomes

Understand basic concepts of machine learning:

• Supervised and unsupervised learning
• Difference between models and learners, training and test errors, etc.
• Over- and underfitting
• Hyperparameter tuning
• Performance evaluatio

Know the major machine learning methods:

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Boosting
• Support Vector Machines
• Artificial Neural Networks

Be able to perform machine learning analyses in R:

• Model fitting
• Hyperparameter tuning
• Performance evaluation
• Benchmarking
• Visualizing results