Machine Learning and Deep Learning Series: Supervised Learning - Regression

Regression, the third lab of the Machine Learning (ML) 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 K-nearest neighbors (KNN) 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.

During the Machine Learning and Deep Learning Open Lab Series, 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 an 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. 

Presented by Namig Abbasov with the ASU Library's Unit for Data Science and Analytics team.