Covers the basics of machine learning for engineers, including grading, course requirements, practical examples, AI concepts, and ML applications.
Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.
Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.
Explores machine learning applications in earthquake engineering, covering ground shaking analysis, experimental approaches, and collapse prediction projects.
Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
