Course

Course Summary
Credit Type:
Course
ACE ID:
NNCS-5222
Organization's ID:
MATH3035
Organization:
National Cryptologic University
Location:
Classroom-based
Length:
40
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 1 Applied Linear Algebra
Description

Objective:

The purpose of this course is to refresh the students' vocabulary and intuition regarding linear algebra, in preparation for entry into the Statistical Analysis and Data Mining course (MATH4350). Students will gain a geometric understanding of matrix equations that occur in a statistical setting and intuition about computer linear algebra routines they are already using in their work.

Learning Outcomes:

  • compute the eigen-structure of any two-by-two matrix, and diagonalize the matrix
  • articulate the geometric action of diagonal, orthogonal matrices
  • explain the Singular Value Decomposition (SVD) of a matrix: (1) geometrically in terms of the action of diagonal and orthogonal matrices and (2) by giving an application
  • state the Spectral theorem and describe what it means in terms of the statistical analysis of data

General Topics:

  • Linear transformations
  • Norms, determinants, geometry with applications to special types of matrices
  • Singular value decomposition
  • Eigenvalues and diagonalization of matrices
  • Applications of the above topics to data mining methodologies
  • Matrix factorization
  • Spectral theorem
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Classroom Exercise
  • Computer Based Training
  • Discussion
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Examinations

Minimum Passing Score:

70%
Supplemental Materials