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

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=== Schedule ===
 
=== Schedule ===
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| W 9/18 || Evaluating and comparing classifiers || William || HW: Implement logistic regression
 
| W 9/18 || Evaluating and comparing classifiers || William || HW: Implement logistic regression
 
|-  
 
|-  
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| M 9/23 || Neural networks || Eric
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| M 9/23 || Neural networks - 1  || Eric
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|-  
 
−
| W 9/25 || K-nearest neighbor classifiers || Eric || HW: Compare several classifiers
 
−
|-
 
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| M 9/30 || Decision trees || Eric
 
 
|-  
 
|-  
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| W 10/2 || PAC-learning and learning theory || William || HW: worksheet on theory
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| W 9/25 || Neural networks - 2 || Eric || HW: Compare some classifiers    
 
|-  
 
|-  
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| M 10/7 || Bias-variance and linear regression || William  
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| M 9/30  || K-nearest neighbor classifiers || Eric
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|-
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|-                                                                                    
−
| W 10/9  || K-means and Mixture models || Eric
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| W 10/2  || Decision trees || Eric    
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|-
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|-                                                                                    
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| M 10/14 || Dimensionality Reduction || Eric
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| M 10/7 || PAC-learning and learning theory || William
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|-  
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|-                                                                                    
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| W 10/16 || Graph-based semi-supervised learning || William
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| W 10/9  || Linear regression || William  || HW: worksheet on theory
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|-  
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|-                                                                                    
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| M 10/21  || Modeling distributions with Bayes Nets and Markov Fields - 1 || Eric
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| M 10/14 || Bias-variance and linear regression || William  
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|-  
+
|-    
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| W 10/23  || Modeling distributions with Bayes Nets and Markov Fields - 2 || Eric
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| W 10/16 || K-means and Mixture models || Eric    
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|-
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|-                                                                                    
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| M 10/28 || Topic models - 1 || William
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| M 10/21 || Dimensionality Reduction || Eric    
−
|-
+
|-                                                                                    
−
| W 10/30 || Topic models - 2|| William
+
| W 10/23 || Graph-based semi-supervised learning || William    
−
|-  
+
|-                                                                                    
−
| M 11/4 || HMMs || Eric
+
| M 10/28 || Modeling distributions with Bayes Nets and Markov Fields - 1 || Eric    
−
|-
+
|-                                                                                    
−
| W 11/6
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| W 10/30 || Modeling distributions with Bayes Nets and Markov Fields - 2 || Eric    
−
|-
+
|-                                                                                    
−
| M 11/11
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| M 11/4 || Topic models - 1 || William    
 +
|-                                                                                    
 +
| W 11/6  || Topic models - 2|| William    
 +
|-                                                                                    
 +
| M 11/11    HMMs || Eric                                                                  
 
|-
 
|-
 
| W 11/13
 
| W 11/13

Revision as of 16:07, 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

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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 - 1 Eric
W 9/25 Neural networks - 2 Eric HW: Compare some classifiers
M 9/30 K-nearest neighbor classifiers Eric
W 10/2 Decision trees Eric
M 10/7 PAC-learning and learning theory William
W 10/9 Linear regression William HW: worksheet on theory
M 10/14 Bias-variance and linear regression William
W 10/16 K-means and Mixture models Eric
M 10/21 Dimensionality Reduction Eric
W 10/23 Graph-based semi-supervised learning William
M 10/28 Modeling distributions with Bayes Nets and Markov Fields - 1 Eric
W 10/30 Modeling distributions with Bayes Nets and Markov Fields - 2 Eric
M 11/4 Topic models - 1 William
W 11/6 Topic models - 2 William
M 11/11 HMMs Eric
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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