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: 2024-01-19 12:44PM

Semesters offered:

• Spring 2024
• Spring 2023
• Spring 2021
• Spring 2020
• Spring 2019
• Spring 2018
• Spring 2017
• Spring 2016
• Spring 2015
• Spring 2014
• Spring 2013
• Spring 2011
• Spring 2010
• Spring 2009
• Spring 2008
• Spring 2007
• Spring 2005
• Spring 2003
• Spring 2002
• Spring 2001
• Spring 2000
• Spring 1997
• Spring 1995
• Spring 1993