Introduction to Pattern Recognition and Machine Learning

Introduction to Pattern Recognition and Machine Learning
Course Number(s): 
Stat 161, Stat 261
Instructor(s): 

Semester: 

  • Spring 2014

Course Level: 

  • Graduate, Undergraduate
Course Description: 

Introduction to pattern analysis and machine intelligence designed for advanced undergraduate and graduate students. Topics include Bayes decision theory, learning parametric distributions, non-parametric methods, regression, Adaboost, perceptrons, support vector machines, principal components analysis, nonlinear dimension reduction, independent component analysis, K-means analysis, and probability models.