From f4c547afe39a705b143d1531a13cfa2f0a130614 Mon Sep 17 00:00:00 2001 From: Prakhar Srivastav Date: Tue, 30 Dec 2014 10:38:05 +0300 Subject: [PATCH] Update README.md --- README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index a8b2065..eacb758 100644 --- a/README.md +++ b/README.md @@ -243,11 +243,8 @@ Courses - [Experiences](http://philipmjohnson.github.io/ics314f13/experiences/) - [Assessments](http://philipmjohnson.github.io/ics314f13/assessments/) - [COMS 4771](http://www.cs.columbia.edu/~jebara/4771/index.html) **Machine Learning** *Columbia University* - - Taught by [Tony Jebara](http://www.cs.columbia.edu/~jebara/resume.html) - - This course introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. - - [Lectures](http://www.cs.columbia.edu/~jebara/4771/handouts.html) - - + - Course taught by [Tony Jebara](http://www.cs.columbia.edu/~jebara/resume.html) introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. + - [Lectures and Assignments](http://www.cs.columbia.edu/~jebara/4771/handouts.html) -----