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

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!  What's new
 
!  What's new
 
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| 9/2 || ''No class - Labor day'' ||
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| M 9/2 || ''No class - Labor day'' ||
 
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| 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William  
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| W 9/4 || [[10-601 Introduction to Probability|Overview and Intro to Probability]] || William  
 
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| 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William
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| M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William
 
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| 9/11 || [[10-601 Logistic Regression|Logistic Regression]] || William
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| W 9/11 || [[10-601 Logistic Regression|Logistic Regression]] || William
 
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| 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William
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| M 9/16 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William
 
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| 9/18 || Evaluating and comparing classifiers || William
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| W 9/18 || Evaluating and comparing classifiers || William
 
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| 9/23 || Neural networks || Eric
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| M 9/23 || Neural networks || Eric
 
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| 9/25 || K-nearest neighbor classifiers || Eric
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| W 9/25 || K-nearest neighbor classifiers || Eric
 
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| 9/30 || Decision trees || Eric
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| M 9/30 || Decision trees || Eric
 
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| 10/2 || PAC-learning and learning theory || William
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| W 10/2 || PAC-learning and learning theory || William
 
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| 10/7 || Bias-variance and linear regression || William
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| M 10/7 || Bias-variance and linear regression || William
 
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| 10/9  || K-means and Mixture models || Eric
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| W 10/9  || K-means and Mixture models || Eric
 
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| 10/14 || Dimensionality Reduction || Eric
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| M 10/14 || Dimensionality Reduction || Eric
 
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| 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric
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| W 10/16 || Modeling distributions with Bayes Nets and Markov Fields || Eric
 
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| 10/21 || Learning with graphical models || Eric
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| M 10/21 || Learning with graphical models || Eric
 
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| 10/23  || Topic models || William
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| W 10/23  || Topic models || William
 
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| 10/28  || HMMs || Eric
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| M 10/28  || HMMs || Eric
 
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| 10/30
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| W 10/30
 
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| 11/4
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| M 11/4
 
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| 11/6
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| W 11/6
 
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| 11/11
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| M 11/11
 
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| 11/13
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| W 11/13
 
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| 11/18
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| M 11/18
 
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| 11/20
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| W 11/20
 
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|-  
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| 11/25
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| M 11/25
 
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| 11/27 || ''Thanksgiving - class cancelled'' ||  
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| W 11/27 || ''Thanksgiving - class cancelled'' ||  
 
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| 12/2
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| M 12/2
 
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| 12/4
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| W 12/4
 
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Revision as of 15:48, 31 July 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 What's due What's new
M 9/2 No class - Labor day
W 9/4 Overview and Intro to Probability William
M 9/9 The Naive Bayes algorithm William
W 9/11 Logistic Regression William
M 9/16 The Perceptron algorithm William
W 9/18 Evaluating and comparing classifiers William
M 9/23 Neural networks Eric
W 9/25 K-nearest neighbor classifiers Eric
M 9/30 Decision trees Eric
W 10/2 PAC-learning and learning theory William
M 10/7 Bias-variance and linear regression William
W 10/9 K-means and Mixture models Eric
M 10/14 Dimensionality Reduction Eric
W 10/16 Modeling distributions with Bayes Nets and Markov Fields Eric
M 10/21 Learning with graphical models Eric
W 10/23 Topic models William
M 10/28 HMMs Eric
W 10/30
M 11/4
W 11/6
M 11/11
W 11/13
M 11/18
W 11/20
M 11/25
W 11/27 Thanksgiving - class cancelled
M 12/2
W 12/4

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

Another view of classification:

Summary:

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