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

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| W 9/18 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  HW: Implement two learners
 
| W 9/18 ||  [[10-601 Logistic Regression|Logistic Regression]] || William ||  HW: Implement two learners
 
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| M 9/23  
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| M 9/23 ||  Linear regression and BackProp || Eric
 
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| W 9/25  
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| W 9/25 || Neural networks and Deep Belief Networks || Eric || HW
 
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| M 9/30  
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| M 9/30 || K-NN, Decision Trees, and Kernels || William ||
 
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|-                                                                                      
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| W 10/2    
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| W 10/2 || Comparing Classifiers Experimentally  || William || HW    
 
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|-                                                                                      
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| M 10/7 ||  .... || Eric ''(William out)''
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| M 10/7 ||  PAC Learning || Eric ''(William out)''
 
|-                                                                                      
 
|-                                                                                      
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| W 10/9  || ...  || Eric ''(William out)''
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| W 10/9  || Bias-Variance Decomposition || Eric ''(William out)''
 
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|-                                                                                      
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| M 10/14  
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| M 10/14 || Ensemble Learning Techniques 1 || William
 
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| W 10/16  
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| W 10/16 || Ensemble Learning Techniques 2 || William
 
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|-                                                                                      
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| M 10/21    
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| M 10/21 || Unsupervised Learning: k-Means and Mixtures || Eric    
 
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|-                                                                                      
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| W 10/23   
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| W 10/23  || Unsupervised Learning: Dimensionality Reduction|| Eric
 
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|-                                                                                      
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| M 10/28
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| M 10/28 || Semi-Supervised Learning || William
 
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| W 10/30  
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| W 10/30 || Collaborative Filtering and Matrix Factorization || William
 
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| M 11/4      
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| M 11/4 || Graphical Models 1 || Eric      
 
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|-                                                                                      
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| W 11/6    
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| W 11/6 || Graphical Models 2 || Eric    
 
|-                                                                                      
 
|-                                                                                      
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| M 11/11                                            
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| M 11/11 || HMMS, Sequences, and Structured Output Prediction || William                                           
 
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|-
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| W 11/13 || ... || Eric  ''(William out)''                 
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| W 11/13 || Topic Models|| Eric  ''(William out)''                 
 
|-
 
|-
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| M 11/18
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| M 11/18 || Topic Models || Eric
 
|-
 
|-
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| W 11/20
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| W 11/20 || Review Session/Special Topics || Eric
 
|-  
 
|-  
−
| M 11/25
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| M 11/25 || Final Exam ||
 
|-
 
|-
 
| W 11/27 || ''No class - Thanksgiving'' ||  
 
| W 11/27 || ''No class - Thanksgiving'' ||  
 
|-  
 
|-  
−
| M 12/2
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| M 12/2 || Markov Decision Processes and Reinforcement Learning || Eric
 
|-
 
|-
−
| W 12/4
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| W 12/4 || Scalable Learning and Parallelization || William
 
|}
 
|}
  

Revision as of 10:34, 27 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

TAs and Eric: For now, let's use the Google Doc Spreadsheet to plan the lectures. Later we can migrate to the wiki schedule below - but it's a little hard to swap things around in the wiki format

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 Linear regression and BackProp Eric
W 9/25 Neural networks and Deep Belief Networks Eric HW
M 9/30 K-NN, Decision Trees, and Kernels William
W 10/2 Comparing Classifiers Experimentally William HW
M 10/7 PAC Learning Eric (William out)
W 10/9 Bias-Variance Decomposition Eric (William out)
M 10/14 Ensemble Learning Techniques 1 William
W 10/16 Ensemble Learning Techniques 2 William
M 10/21 Unsupervised Learning: k-Means and Mixtures Eric
W 10/23 Unsupervised Learning: Dimensionality Reduction Eric
M 10/28 Semi-Supervised Learning William
W 10/30 Collaborative Filtering and Matrix Factorization William
M 11/4 Graphical Models 1 Eric
W 11/6 Graphical Models 2 Eric
M 11/11 HMMS, Sequences, and Structured Output Prediction William
W 11/13 Topic Models Eric (William out)
M 11/18 Topic Models Eric
W 11/20 Review Session/Special Topics Eric
M 11/25 Final Exam
W 11/27 No class - Thanksgiving
M 12/2 Markov Decision Processes and Reinforcement Learning Eric
W 12/4 Scalable Learning and Parallelization William

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

Another view of classification:

Summary:

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