Complementary Topics in Linear Algebra (inner Product Spaces, Norms, Orthonormal Bases, Eigen-values), Matrix Decomposition (spectral Decomposition Theorem, Svd), Linear Systems (gauss Elimination Process), Lest Squares, Iterative Methods (power Method), Linear Programming (simplex Method, Duality, Applications to Classification And Sparse Solutions to Linear Systems), Perturbation Theorems For Matrix Decomposition, Introduction to Tensors.

Faculty: Data and Decision Sciences
|Undergraduate Studies

Pre-required courses

(94345 - Discrete Mathematics (for I.e) and 104032 - Calculus 2m and 104166 - Algebra Am and 234117 - Introduction to Computer Science H) or (94345 - Discrete Mathematics (for I.e) and 104016 - Algebra 1/extended and 104022 - Differential and Integral Calculus 2m and 234221 - Introduction to Computer Science N) or (94346 - Discrete Mathematics and 104019 - Linear Algebra M and 104020 - Calculus 2n and 234111 - Introduction to Computer Science) or (94346 - Discrete Mathematics and 104016 - Algebra 1/extended and 104020 - Calculus 2n and 234221 - Introduction to Computer Science N) or (94346 - Discrete Mathematics and 104019 - Linear Algebra M and 104020 - Calculus 2n and 234221 - Introduction to Computer Science N) or (94347 - Discrete Mathematics and 104016 - Algebra 1/extended and 104022 - Differential and Integral Calculus 2m and 234221 - Introduction to Computer Science N)


Course with no extra credit

95296 - Algebric Methods For Data Science 234125 - Numerical Algorithms


Course with no extra credit (contained)

238125 - Numerical Algorithms M


Semestrial Information