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ABOUT THE COURSE:
This course introduces basic linear algebra, matrix decompositions, least squares, associated algorithms, and applications to optimisation, machine learning and probability; specially tailored for engineering students. No prior exposure to optimization or machine learning is required.
The material is organised into three parts: the basics, matrix decompositions and least squares, and connections and applications.
INTENDED AUDIENCE:Undergraduate and first-year postgraduate students in EE, CSE, and AI / Data Science.
PREREQUISITES:Familiarity with basic mathematical notation and arguments, basic probability, and basic multivariate calculus. Many examples draw from data science, but prior exposure to these topics is not required.
INDUSTRY SUPPORT: This is a foundational course that improves the general understanding of other core engineering techniques involving linear algebra. In addition, this course will draw a lot of examples from Machine Learning, which may be useful for students interested in pursuing further courses in AI.
This course introduces basic linear algebra, matrix decompositions, least squares, associated algorithms, and applications to optimisation, machine learning and probability; specially tailored for engineering students. No prior exposure to optimization or machine learning is required.
The material is organised into three parts: the basics, matrix decompositions and least squares, and connections and applications.
INTENDED AUDIENCE:Undergraduate and first-year postgraduate students in EE, CSE, and AI / Data Science.
PREREQUISITES:Familiarity with basic mathematical notation and arguments, basic probability, and basic multivariate calculus. Many examples draw from data science, but prior exposure to these topics is not required.
INDUSTRY SUPPORT: This is a foundational course that improves the general understanding of other core engineering techniques involving linear algebra. In addition, this course will draw a lot of examples from Machine Learning, which may be useful for students interested in pursuing further courses in AI.
