CISC371, Nonlinear Data Analysis: Fall 2020

Course Description:

Nonlinear data analysis encompasses a broad range of techniques that are based in numerical optimization. This course will cover some techniques that specifically address sets of data in vector spaces.

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.

For basic material in data analytics, students can expect to be instructed in:

  • elements of optimization for scalar arguments
  • basic methods for optimization in Euclidean spaces
  • applications in "shallow" neural networks
  • basic methods for constrained optimization using KKT conditions
  • applications in constrained regression and SVM classification
  • The course contents, including the schedule, notes, and supplementary material, are provided in the onQ learning management system.

    Calendar Description:

    Methods for computational optimization, particularly examining nonlinear functions of vectors. Topics may include: unconstrained optimization; first-order methods; second-order methods; convex problems; equality constraints; inequality constraints; applications in machine learning.

    Course Lectures:

    Each student is expected to have read the relevant class notes and viewed the recorded lecture material before attending class, and to have understood the important definitions and concepts. The class time will concentrate mainly on the use of concepts to solve problems as exercises. Definitional material will not be covered because students are expected to learn the definitions before attending class.

    Each student is responsible for transcribing class 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.

    Recent Course News (as of April 29, 2018)

  • The course will next be offered in September, 2020

  • Last updated