This course introduces the theory and practice of machine learning for graduate engineering students. The course presents tools for analysis and prediction using machine learning techniques, including regression, support vector machines, hidden Markov models, ensemble methods, supervised and unsupervised learning. The course will be focused on the fundamentals of these techniques, the advantage, disadvantage and usage context of each technique, and their application in the engineering field. Engineering examples for each topic will be provided. PREREQUISITE: Students must have taken a probability and statistics course (e.g. MTHE 367 or equivalent.
This course introduces the theory and practice of machine learning for graduate engineering students. The course presents tools for analysis and prediction using machine learning techniques, including regression, support vector machines, hidden Markov models, ensemble methods, supervised and unsupervised learning. The course will be focused on the fundamentals of these techniques, the advantage, disadvantage and usage context of each technique, and their application in the engineering field. Engineering examples for each topic will be provided. PREREQUISITE: Students must have taken a probability and statistics course (e.g. MTHE 367 or equivalent.
