Course Description

Statistical pattern recognition is a powerful analytical approach that leverages statistics to uncover patterns within data. It involves extracting meaningful insights from observations and making predictions based on these patterns. Key components of the process include feature extraction, classification, and decision-making. By applying statistical models and algorithms, we can identify hidden regularities in large datasets.


  • Introduction to basics concepts and definition
  • Bayesian Decision Theory and Discrimination Functions
  • Linear Classifier
  • Nonlinear Classifier
  • Introduction to Deep Learning
  • Feature Selection and Feature Reduction
  • Feature Generation I, Basic
  • Feature Generation II, Application
  • Clustering and Validation


Lecture Videos (as taught 1400-1401-1):