Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"

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[http://prezi.com/tneo-hju3esr/?utm_campaign=share&utm_medium=copy&rc=ex0share Link to Prezi Overview]
 
[http://prezi.com/tneo-hju3esr/?utm_campaign=share&utm_medium=copy&rc=ex0share Link to Prezi Overview]
  
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Revision as of 11:09, 1 August 2013

This is the syllabus for Machine Learning 10-601 in Fall 2013.

Prezi Overview of All the Topics in the Course

Link to Prezi Overview

Schedule

Schedule for 10-601 in Fall 2013
Date Topic Lecturer Assignment
M 9/2 No class - Labor day
W 9/4 Overview and Intro to Probability William HW: worksheet on probabilities
M 9/9 The Naive Bayes algorithm William
W 9/11 The Perceptron algorithm William HW: Implement two learners
M 9/16 The Perceptrons, SVMs, and other Margin Classifiers William
W 9/18 Logistic Regression William HW: Implement two learners
M 9/23
W 9/25
M 9/30
W 10/2
M 10/7 .... Eric (William out)
W 10/9 ... Eric (William out)
M 10/14
W 10/16
M 10/21
W 10/23
M 10/28
W 10/30
M 11/4
W 11/6
M 11/11
W 11/13 ... Eric (William out)
M 11/18
W 11/20
M 11/25
W 11/27 No class - Thanksgiving
M 12/2
W 12/4

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

Another view of classification:

Summary: