Texas A&M University 3 credits ECEN
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.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.