Pattern Recognition and Analysis

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.

Instructor(s)

MIT Course Number

MAS.622J / 1.126J

As Taught In

Fall 2006

Level

Graduate

Cite This Course

Course Features

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.

Archived Versions

Faculty and Staff, Media Lab, Bo Morgan, Rosalind Picard, and Andrea Thomaz. MAS.622J Pattern Recognition and Analysis, Fall 2006. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/media-arts-and-sciences/mas-622j-pattern-recognition-and-analysis-fall-2006 (Accessed). License: Creative Commons BY-NC-SA


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