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

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| M 9/30 || Decision trees || Eric
 
| M 9/30 || Decision trees || Eric
 
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| W 10/2 || PAC-learning and learning theory || William
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| W 10/2 || PAC-learning and learning theory || William || HW: worksheet on theory
 
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| M 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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| W 10/9  || K-means and Mixture models || Eric
 
| W 10/9  || K-means and Mixture models || Eric
 
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| M 10/14 || Dimensionality Reduction || Eric
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| M 10/14 || Dimensionality Reduction || Eric
 
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| W 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 - 1 || Eric
 
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| M 10/21 || Learning with graphical models || Eric
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| M 10/21 || Modeling distributions with Bayes Nets and Markov Fields - 2 || Eric
 
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| W 10/23  || Topic models || William
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| W 10/23   
 
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| M 10/28  || HMMs || Eric
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| M 10/28  || Topic models || William
 
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| W 10/30
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| W 10/30 || HMMs || Eric
 
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| M 11/4
 
| M 11/4

Revision as of 15:58, 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 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 Logistic Regression William
W 9/18 Evaluating and comparing classifiers William HW: Implement logistic regression
M 9/23 Neural networks Eric
W 9/25 K-nearest neighbor classifiers Eric HW: Compare several classifiers
M 9/30 Decision trees Eric
W 10/2 PAC-learning and learning theory William HW: worksheet on theory
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 - 1 Eric
M 10/21 Modeling distributions with Bayes Nets and Markov Fields - 2 Eric
W 10/23
M 10/28 Topic models William
W 10/30 HMMs Eric
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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