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 – 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.