Lecture
This lecture delves into machine learning applications in the modeling of materials and molecules, focusing on regression and classification tasks. The instructor discusses predicting atomization energies based on geometry, using large datasets and advanced theories like couple cluster theory. The lecture also covers the application of SVM for classifying molecules as active or inactive based on their proximity to receptors, showcasing the importance of feature selection and symmetry adaptation in building accurate models.