CISC 271/3.0 Linear Data Analytics
Original Author: Randy Ellis Calendar Description Elements of linear algebra for data analysis, including: solution of linear equations; vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data. Prerequisites: Level 2 or above and C in {[CISC 101/3.0 or CISC 121/3.0] and [MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0] and [MATH 120/6.0 or MATH 121/6.0 or (MATH 123/3.0 and MATH 124/3.0) or MATH 126/6.0]. Exclusions: No more than 3.0 units from CISC 271/3.0; MATH 272/3.0; PHYS 213/3.0; PHYS 313/3.0. Learning hours: 120 (36L; 84P)
Degree Planning
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Outline This course will explore techniques for analyzing sets of data that are in vectors. These techniques are primarily the use of linear algebra, which will be applied to data gathered by empirical studies. To test, implement, and analyze this material, MATLAB will be used as an interactive tool and programming language. Students are expected to learn basic MATLAB on their own. Some tutorial information will be provided early in the course. For basic material in data analytics, students can expect to be instructed in:
For basic material in machine learning, students can expect to be instructed in:
