CISC271, Linear Data Analysis: Winter 2024

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.

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.

Brief Description:

Linear data analysis encompasses a broad range of techniques that are used to analyse structred data. This course will cover some techniques that specifically address sets of data in vectors.

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:

  • organization of data into vectors and matrices
  • description of vectors in a space
  • minimal space descriptors
  • linear relations
  • spaces of solutions to linear relations
  • For basic material in machine learning, students can expect to be instructed in:

  • linear regression
  • polynomial regression
  • data reduction by PCA
  • SVD methods
  • elements of artificial neural networks
  • 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.

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    Ne Queen's University e’tho nón:we nikanónhsote tsi nón:we ne Haudenosaunee táhnon Anishinaabek tehatihsnonhsáhere ne onhwéntsya.

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