Machine Learning (ML) is seen as a subset of Artificial Intelligent (AI) where intelligent algorithms autonomously learn concepts and latent patterns from data to perform different tasks. ML models utilize a variety of techniques to intelligently handle large and complex amounts of information to make human-like decisions. ML models explore the fundamental principles in many disciplines, including statistics, graph theory, mathematical optimization, knowledge representation, planning and control, data analytics, causal inference, computer systems, computer vision, and natural language processing in order to solve complex problems. Machine learning algorithms can impact many applications relying on all sorts of data, such as medical health data, scientific data, financial data, security data, weather data, energy data, and data from any active or passive sensing instruments. WVU faculties have developed novel ML algorithms for specific tasks in health sciences, defense, homeland security, cybersecurity and energy.