Welcome to the Machine Learning and Deep Learning Open Lab Series! During this series, Dr. Namig Abbasov offers seven open labs to introduce participants to core concepts and techniques in Machine Learning (ML) and Deep Learning. These open labs will prioritize intuitive understanding of machine learning algorithms and deep learning approaches. These are intended to complement machine learning and deep learning courses taught at ASU by focusing on intuitive explanations of difficult concepts and examples with analogical illustrations. Below, you will find descriptions for each open lab of the series. Each open lab session is one hour long, allowing us to explore these topics as thoroughly as time permits.

Supervised Learning – Regression, the third lab of the Machine Learning and Deep Learning Series will cover ML models for regression tasks. The session begins with the fundamentals of linear regression and progresses to polynomial regression and regularization methods such as Lasso and Ridge regression. In addition, decision trees, support vector machines, and KNNs will be introduced to demonstrate their effectiveness in regression tasks. The lab will also emphasize evaluating model performance using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared values. Through practical examples, participants will learn to identify and mitigate common pitfalls in regression analysis.