About the Course
Machine learning (ML) 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 accompanying examples of the application of machine learning to modern problems in the fields of petroleum engineering, microseismic monitoring, underground storage, and geothermal energy. 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). Although machine learning typically requires HPC resources and advanced programming skills, the course is designed in a way that trainees only need basic programming skills in Python.