MATH 215: Introduction to Linear Algebra With Applications in Statistics

Welcome to the webpage for Introduction to Linear Algebra With Applications in Statistics (MATH 215) at the FAES Graduate School at NIH. It describes the course as taught between 2019 and 2021, and may no accurately reflect changes made after that. This course is aimed at an audience with diverse backgrounds, starting from the basic concepts of vectors, systems of equations, and matrices, and progressively moving into more advanced concepts and applications. Renewed interest in Linear Algebra has been seen in the recent years as it underlies many machine learning applications; however, it also underlies classic statistical methods and many kinds of mathematical models. It is a core subject not only for mathematicians and statisticians, but also scientists in all areas who wish to move beyond standard method application and develop more sophisticated analysis frameworks. The concepts of a space and subspace also underpin the study of nonlinear descriptions like that of vector calculus (covered in MATH 128) and more advanced subjects like differential geometry.

The content of standard Linear Algebra textbooks is covered in this course, with emphasis given to core concepts as well applications to statistics and numerical computation in the natural sciences – but without detriment to a sufficient exposure to more advanced concepts. A textbook in linear algebra is required for this course. The adopted and recommended book is Introduction to Linear Algebra by Gilbert Strang. An alternative text is Linear Algebra by Jim Hefferon, adopted in previous semesters, which has an open online version and affordable hard copies.

Here you can find a typical syllabus. If you are enrolled in this course you can log into the student course page using this link, or you can enroll here.