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.

Advanced ML - Ensemble Methods and Model Optimization, the fifth lab of the Machine Learning and Deep Learning Series covers advanced machine learning techniques to enhance model performance. Participants will dive into ensemble methods like random forests and gradient boosting algorithms such as XGBoost and LightGBM. The session will also introduce hyperparameter tuning strategies, including GridSearchCV to optimize model parameters effectively. To ensure robust model evaluation, participants will learn cross-validation techniques and how to interpret their results. This lab is geared toward those looking to improve the accuracy and reliability of their machine learning models through state-of-the-art approaches.