Machine Learning and Deep Learning Series: Supervised Learning – Classification

Classification, the second lab of the Machine Learning and Deep Learning Series, shifts the focus to classification models for predicting categorical outcomes. Participants will explore a range of classification algorithms, starting from logistic regression and decision trees to more advanced models like support vector machines (SVMs), and k-nearest neighbors (KNN). Naive Bayes and other classifiers will also be covered to provide a comprehensive understanding of classification methods. The session will teach participants how to evaluate models effectively using metrics such as accuracy, precision, recall, F1-score and confusion matrices.

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.