|CISC271, Linear Data Analysis: Winter 2024
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.
Lecture and Report Materials:
The navigation panel, on the left, leads to a list of the lectures. These are video recordings and notes for the course. Guides to writing reports, for students and for graders, are also on the left.
A great deal of the instructional material is intended for
asynchronous learning. Regularly scheduled lectures are augmented
with instructional videos and written notes. Each student is
expected to view the videos and read the notes before attending
the scheduled class times.
To test, implement, and analyze this material, we will use MATLAB 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:
Course content that relate to assessments are provided in the onQ learning management system that acts as a paywall for students who are enrolled in this course. Course content includes: scheduled assessments; non-credit homework; statements of assignment and preparatory material; and other supplementary material for linear data analysis.
Queen's University is situated on the territory of the Haudenosaunee and Anishinaabek.
Ne Queen's University e’tho nón:we nikanónhsote tsi nón:we ne Haudenosaunee táhnon Anishinaabek tehatihsnonhsáhere ne onhwéntsya.
Gimaakwe Gchi-gkinoomaagegamig atemagad Naadowe miinwaa Anishinaabe aking.