University at Buffalo 3 credits Engineering & Applied Sciences
An introduction to the mathematical theory and computational methodology at the heart of statistical learning. This first semester considers supervised learning, including topics classification β support vector machines, k-nearest neighbors, Naive Bayes, logistic regression, tree methods and forests, bagging and ensemble methods, GPs and neural networks β and methods for validation and testing. R programming will be used. Prerequisite experience: EAS MS-DS student, or MTH 142, MTH 309, MTH 411 or equivalent, or permission 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.