CISC271, Linear Data Analysis: Winter 2025

Calendar Description:

Elements of linear algebra for artificial intelligence, including: vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data; validation and cross-validation.

Instructional Materials:

The navigation panel, on the left, leads to a list of the lectures. These are notes and video recordings for the course. The notes are essential material. The videos are intended to supplement — but not replace — the live classes.

Brief Description:

Linear methods encompassed a broad range of techniques that are used to analyse structured data. This course will cover some techniques that specifically address sets of data in vectors. The purpose is to provide preparation for subsequent courses in machine learning.

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, 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
  • validation and cross-validation
  • elements of artificial neural networks

    • Course content that relate to assessments are provided in the onQ learning management system, which acts as a paywall for students enroled in this course. Course content includes: scheduled assessments; non-credit homework; statements of assignment and preparatory material; and other supplementary material in linear methods for artificial intelligence.

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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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