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

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| W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William ||  HW: Implement two learners
 
| W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William ||  HW: Implement two learners
 
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| M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William ||                  
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| M 9/16 || The Perceptrons, SVMs, and other Margin Classifiers || William ||  
 
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|-  
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| W 9/18 || Evaluating and comparing classifiers || William || HW: Implement logistic regression
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| W 9/18 || [[10-601 Logistic Regression|Logistic Regression]] || William ||   HW: Implement two learners
 
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| M 9/23 || Neural networks - 1  || Eric
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| M 9/23  
 
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| W 9/25 || Neural networks - 2 || Eric  || Project plan 1: Deep Networks for Images
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| W 9/25  
 
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| M 9/30 || K-nearest neighbor classifiers || Eric
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| M 9/30  
 
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|-                                                                                      
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| W 10/2 || Decision trees || Eric  || HW: Compare some classifiers        
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| W 10/2    
 
|-                                                                                      
 
|-                                                                                      
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| M 10/7 || Linear regression || William
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| M 10/7 || .... || Eric ''(William out)''
 
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|-                                                                                      
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| W 10/9  || PAC-learning and learning theory || William || HW: worksheet on theory
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| W 10/9  || ...  || Eric ''(William out)''
 
|-                                                                                      
 
|-                                                                                      
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| M 10/14 || Bias-variance and linear regression || William
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| M 10/14  
 
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| W 10/16 || Ensembles and boosting || William || Project plan 2: Robust learners
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| W 10/16  
 
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|-                                                                                      
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| M 10/21 || K-means and Mixture models || Eric    
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| M 10/21    
 
|-                                                                                      
 
|-                                                                                      
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| W 10/23  || Dimensionality Reduction || Eric || HW: Unsupervised learning
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| W 10/23   
 
|-                                                                                      
 
|-                                                                                      
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| M 10/28 || Graph-based semi-supervised learning || William
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| M 10/28
 
|-                                                                                      
 
|-                                                                                      
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| W 10/30 || Modeling distributions with Bayes Nets and Markov Fields - 1 || Eric || HW: worksheet on Bayes nets
+
| W 10/30  
 
|-                                                                                      
 
|-                                                                                      
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| M 11/4 || Modeling distributions with Bayes Nets and Markov Fields - 2 || Eric        
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| M 11/4      
 
|-                                                                                      
 
|-                                                                                      
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| W 11/6 || Topic models - 1 || Eric    
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| W 11/6    
 
|-                                                                                      
 
|-                                                                                      
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| M 11/11 || Topic models - 2|| Eric                                               
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| M 11/11                                            
 
|-
 
|-
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| W 11/13 || HMMs || Eric                
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| W 11/13 || ... || Eric ''(William out)''               
 
|-
 
|-
 
| M 11/18
 
| M 11/18

Revision as of 11:07, 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

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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 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:

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