CISC271, Linear Data Analysis: Winter 2020

Course 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
  • Perceptron and SVM principles
  • The course contents, including the schedule, notes, and supplementary material, are provided in the onQ learning management system.

    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.

    Course Lectures:

    Each student is expected to have read the relevant textbook sections before attending class, and to have understood the important definitions and concepts. The lecture will concentrate mainly on the use of terms and concepts. Definitional material will not be covered because students are expected to learn the definitions before attending class.

    Each student is responsible for transcribing lecture material. The instructor will not scan or otherwise copy the lecture notes, which are intended to reflect and supplement the on-line class notes available in onQ.


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