Course Details

  • Course Number: 16:198:501
  • Course Type: Graduate
  • Semester 1: Fall
  • Semester 2: Spring
  • Credits: 3
  • Description:

    Focus on the use of linear algebra and statistical conceptual tools in machine learning and data mining practice. Topics include Matrix Factorizations, Bayesian approaches to Hypothesis testing - Parameter Estimation, Kernels, Density Estimation, Gradient Descent, and Neural Networks.

  • Category: A (M.S.)
  • Prerequisite Information:

    198:205 (Discrete Math), 640:152 (Calculus II), 960:580 (Basic Probability) or equivalents

  • Expected Work: Approximately 5 Homework assignments, 3 small group projects, Midterm and Final exams