MATH 3790 APPLIED MACHINE LEARNING
Development of parametric and non-parametric supervised learning models such as logistic regression, neutral networks, support vector machines, and kernels. Unsupervised learning models such as clustering, principle components, recommender systems, and deep learning. Bias-variance methods for model analysis. Application of these learning models. TYPE OF COURSE: Elective PREREQUISITES: MATH 2415 and MATH 2995