MAS.622J / 1.126J Pattern Recognition and Analysis

Fall 2006

Parzen window illustration.
Six Gaussians (red) and their sum (blue). The Parzen window density estimate f(x) is obtained by dividing this sum by 6, the number of Gaussians. The variance of the Gaussians was set to 0.5. Note that where the points are denser the density estimate will have higher values.

Course Highlights

This course features problem sets in the assignments section and sample student projects.

Course Description

This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.

Technical Requirements

Special software is required to use some of the files in this course: .dat, .gz, .zip, and .tar.

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Staff

Instructors:
Media Lab Faculty and Staff

Course Meeting Times

Lectures:
Two sessions / week
1.5 hours / session

Recitations:
One session /week
1 hour /session

Level

Graduate