This course is part of the Mathematics for Machine Learning and Data Science Specialization by DeepLearning.AI. After completing this course, learners will be able to: Represent data as vectors and ...

Vectors: Represent quantities with both magnitude and direction, often visualized as arrows. Matrices: Rectangular arrays of numbers used to represent linear transformations and systems of equations.

*Note: This course discription is only applicable to the Computer Science Post-Baccalaureate program. Additionally, students must always refer to course syllabus for the most up to date information.

Self-funded student: register by the 10th of the month, start on the 1st of the next. Funded student: please check the next enrolment deadline and course start date. Mathematics Diagnostic Assessment.

M.Sc. in Applied Mathematics, Technion (Israel Institute of Technology) Ph.D. in Applied Mathematics, Caltech (California Institute of Technology) [1] A. Melman (2023): “Matrices whose eigenvalues are ...

Abstract: This book contains a detailed discussion of the matrix operation, its properties, and its applications in finding the solution of linear equations and determinants. Linear algebra is a ...

Abstract: Sparse linear algebra kernels achieve sub-optimal performance due to their poor cache locality. Matrix reordering is an effective pre-processing optimization that improves cache locality and ...