CS103

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Linear Algebra for Machine Learning

Course (UG/PG)

Undergraduate

Offering Unit/Department

Course Description

This is an introductory course in Linear Algebra, emphasizing the concepts and structures that underpin modern machine learning. It develops an intuitive and mathematical understanding of vectors, matrices, and linear transformations, and shows how they are used to represent data, extract patterns, and build predictive models. The course prepares students for more advanced work in machine learning and data-driven computing.

Course Learning Outcomes

1. Determine the existence and uniqueness of the solution of a linear system, and find all solutions by choosing an effective method such as Gaussian elimination, factorization or diagonalization, etc.

2. Test for linear independence of vectors, orthogonality of vectors and vector spaces.

3. Determine the rank, determinant, inverse, Gram-Schmidt orthogonalization and different factorizations of a matrix.

4. Visualize and compute the four fundamental subspaces of a matrix, and identify their relation to systems of linear equations, and find their dimension and basis.

5. Describe the use of mathematical techniques from linear algebra as applied to computer applications.

6. Compute eigenvalues and eigenvectors of a matrix, use them for diagonalizing, taking its powers, and applying them to solve advanced problems.

7. Identify special properties of a matrix, such as symmetry, positive definiteness, etc., and use this information to facilitate the calculation of matrix characteristics. [Optional Topic]

8. Describe the use of Singular Value Decomposition and Principal Component Analysis in data science algorithms. [Optional Topic]

Discipline-Specific Competencies

Data Analytics, Formal Proof Construction, Algorithm Analysis, Computational Modelling, Pattern Recognition Systems

SMU Graduate Learning Outcomes

Disciplinary Knowledge, Critical thinking & problem solving, Self-directed learning

Grading Basis

GRD - Graded