CISC271, Scientific Computing: Winter 2018

Course Description:

Scientific computing encompasses a broad range of techniques that are used to develop computer programs with a certain level of mathematical complexity. This course will cover those techniques known as numerical methods, specifically addressing 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.

    Last updated February 18, 2018