Machine Learning Engineering

Catalog description

Credits 3. 3 Lecture Hours. Emphasis on fundamental theory for learning supervised classification-regression models; covers Bayes classifier, maximum-likelihood estimation, least squares, Probably Approximately Correct Learning, empirical risk minimization, Vapnik-Chervonenkis dimension, computational learning, structural risk minimization, regularization, cross-validation, acyclic feedforward networks, completeness of neural networks, backpropagation algorithm, gradient descent, stochastic gradient descent, Convolutional Neural networks, Auto-encoders, Generative Adversarial Networks, support vector machines, kernel-based methods, learning from experts, boosting, Gaussian process-based learning, word embeddings, recurrent neural networks, decision trees, random forests and nearest neighbor classification. Prerequisite: ECEN 303 , MATH 411 , STAT 614 , STAT 615 , or approval of instructor.