Course Information

18-752: Estimation, Detection and Learning

Units:

12

Description:

This course discusses estimation, detection, identification and machine learning, covering a variety of methods, from classical to modern.

In detection, the topics covered include hypothesis testing, Neyman-Pearson detection, Bayesian classification and methods to combine classifiers.

In estimation, the topics include maximum-likelihood and Bayesian estimation, regression, prediction and filtering, Monte Carlo methods and compressed sensing.

In identification and machine learning, topics include Gaussian and low-dimensional models, learning with kernels, support vector machines, neural networks, deep learning, Markov models and graphical models.

Prerequisites: 36-217 and senior or graduate standing

Last Modified: 2017-10-26 2:16PM

Current session:

This course is currently being offered.

Semesters offered:

  • Spring 2018
  • Spring 2017
  • Spring 2016
  • Spring 2015
  • Fall 2014
  • Spring 2014
  • Spring 2013
  • Spring 2011
  • Spring 2010
  • Spring 2009
  • Spring 2008
  • Spring 2007
  • Spring 2006
  • Spring 2005
  • Spring 2004
  • Spring 2003
  • Spring 2002
  • Spring 2001
  • Spring 2000
  • Spring 1998
  • Spring 1997