About the Course
Machine learning has become the main driver of numerous innovative applications: from automated driving and medical devices to industrial automation and electronics. The course offers a comprehensive introduction to machine learning techniques and illustrates the application of machine learning to modern geophysical problems. The goal of the course is to provide trainees with a fundamental understanding of machine learning algorithms sufficient to apply them to solve problems. Topics cover classical supervised learning (linear regression, logistic regression, support vector machine), ensemble methods, neural networks (DNN, CNN, RNN, GAN), and unsupervised learning (clustering, principal component analysis, anomaly detection). The course will also draw from numerous case studies and applications so that trainees will also learn how to apply machine learning algorithms. Although machine learning typically requires HPC resources and advanced programming skills, the course is designed in such a way that trainees only need the basic programming skills in Python.