Machine Learning for Calibration and Classification

Session details:

This course offers a comprehensive overview of modern machine learning with an emphasis on supervised methods. Students begin by establishing a strong foundation in machine learning fundamentals—including essential nomenclature, clear definitions, and key quality metrics—while also exploring the critical concepts of bias vs. variance trade-off and the distinctions between supervised and unsupervised methods.

From there, the curriculum delves into several pivotal machine-learning algorithms:

• Locally Weighted Regression: Techniques for localized modeling using weighted regression and distance measures.

• Support Vector Machines: Core principles, kernel functions, and both classification and regression applications.

• Artificial Neural Networks: A study of various ANN architectures, effective training procedures, strategies to prevent overfitting, and practical deep learning implementations.

• Gradient Boosted Decision Trees: An introduction to decision trees, focusing on gradient boosting concepts and the practical use of tools like XGBoost.