Basic Information
Complementary Topics in Linear Algebra (inner Product Spaces, Norms, Orthonormal Bases, Eigenvalues and Eigenvectors), Matrix Decomposition (spectral Decomposition Theorem, Svd), Linear Systems, Least Squares, Iterative Methods For Singular Value Decomposition, Iterative Methods For Large-scale and Sparse Least Squares Problems, 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 (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
95295 - Algebraic Methods For Data Science 234125 - Numerical Algorithms
Course with no extra credit (contained)
238125 - Numerical Algorithms M
Semestrial Information
Weekly Hours
4 Academic Credit • 3 Lecture Hours • 2 Discussion Hours
Responsible(s)
Dan Garber
Exams
Session A: 12-04-2024 09:00 - 12:00- נהול 214. 215. 216.
- בלומפילד 151. 424. 526.
- דייויס 450. 451. 640. 641.
Registration Groups
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