Units:

12

Description:

Decision theory, parameter estimation, density estimation, non-parametric techniques, supervised learning, linear discriminant functions, clustering, unsupervised learning, artificial neural networks, feature extraction, support vector machines, and pattern recognition applications (e.g., face recognition, fingerprint recognition, automatic target recognition, etc.). 4 hrs. lec.

Prerequisites: 36-217, or equivalent introductory probability theory and random variables course and an introductory linear algebra course and senior or graduate standing.

Last Modified: 2022-05-26 4:06PM

Semesters offered:

• Fall 2022
• Fall 2021
• Fall 2020
• Fall 2019
• Fall 2018
• Fall 2017
• Fall 2016
• Fall 2015
• Fall 2014
• Fall 2013
• Fall 2012
• Fall 2011
• Spring 2011
• Spring 2010
• Spring 2009
• Spring 2008
• Spring 2007
• Spring 2005
• Spring 2004
• Spring 2003
• Spring 2001
• Spring 1999