Application: You will be able to apply these methods to problems of types you have seen and to other related problems in areas such as scientific computing and AI as those arising in the class assignments
Communication : Your understanding of the methods will be allow you to communicate this information to anyone who asks, as measured by course exams.
Polynomial interpolation and approximation including real data approximation using least squares (polynomial regression)
Numerical differentiation and integration
Computational linear algebra (direct and iterative methods) including methods for both scientific computing and data science
Eigenvalues and singular values, including applications to data science and image processing
Analysis: You will have a sufficient understanding of the methods as to be able to compare how they perform.
Comprehension: You will understand the methods in sufficient depth to be able to describe and code them in the class assignments.
Nonlinear systems and optimization including Newtons method and gradient descent for machine learning