Practical Machine Learning for Engineering Analytics

Catalog description

Machine learning is transforming engineering analytics by enabling predictive capabilities and insights from complex datasets. Engineers can analyze data from diverse sources to identify patterns, optimize performance, predict failures, and enhance maintenance strategies. These techniques shift traditional problem-solving approaches, supporting informed decisions and more effective solutions. This course emphasizes practical applications through lectures, case studies, assignments, and projects that mirror real-world challenges. Students will explore supervised and unsupervised learning, classification, regression, clustering, anomaly detection, neural networks, and time-series prediction. The curriculum also covers preprocessing, feature selection, model training, and evaluation. Hands-on projects will allow students to implement algorithms, apply tools, and evaluate results in realistic engineering contexts. By bridging theory with practice, the course provides a comprehensive understanding of how machine learning, AI, and deep learning can solve complex engineering problems and drive innovation in analytics and decision-making.